Full Report.docx

PREDICTING LENGTH OF STAY, FUNCTIONAL OUTCOME, AND AFTERCARE IN THE REHABILITATION OF STROKE PATIENTS.

Abstract: Hospital length of stay (LOS) of patients is an important factor for planning and managing the resource utilization of a hospital. There has been considerable interest in controlling hospital cost and increasing service efficiency, particularly in stroke and cardiac units where the resources are severely limited. This study introduces an approach for early prediction of LOS of stroke patients arriving at the Stroke Unit of King Fahad Bin Abdul-Aziz Hospital, Saudi Arabia. The approach involves a feature selection step based on information gain followed by a prediction model development step using different machine learning algorithms. Prediction results were compared in order to identify the best performing algorithm. Many experiments were performed with different settings. This paper reports the performance results of the two most accurate models. The Bayesian network model with accuracy of 81.28% outperformed C4.5 decision tree model (accuracy 77.1%).

INTRODUCTION: Stroke, also known as cerebrovascular accident (CVA) is a sudden and devastating illness that is characterized by the rapid loss of brain function due to a disruption of blood supply to the brain. This disruption is caused by either ischemia (lack of blood flow) which counts for more than 80% of all strokes [1], blockage of blood flow, or hemorrhage (loss of blood) [2]. Based on previous studies, Stroke is the second cause of death worldwide after cardiac disease, cancer, and chronic lower respiratory disease [3]. The length of stay (LOS) of a stroke patient varies depending on various factors describing the patient’s medical condition [4]. LOS is defined as the number of days a patient is required to stay in a hospital or any healthcare facility for treatment [5], [6]. LOS is an important factor for planning and managing the resource utilization of a hospital [7]. There has been considerable interest in controlling hospital cost and increasing service efficiency, particularly in stroke and cardiac units where the resources are severely limited; thus hospitals try to reduce LOS as much as possible [8]. Moreover, reducing the LOS purportedly yields large cost savings [7], [9]. A model to predict the length of stay (LOS) of stroke patients at an early stage of patient management can effectively help in managing hospital resources and increase efficiency of patient care. Such a prediction model will enable early interventions to reduce complications and shorten patient’s LOS and ensure a more efficient use of hospital facilities. Most previous research uses statistical techniques to identify factors determining the LOS and the prediction of LOS is made using regression models [4], [6], [9], [10]. Recently, with the availability of large amounts of patients’ data, data mining approaches to predict LOS are becoming increasingly promising [5], [11]–[16]. In healthcare, data mining has been used for diagnosis and prognosis of illness and for predicting the outcome of medical procedures [16]–[23]. Data mining techniques such as classification, artificial neural networks,

clustering etc. are used to discover data patterns and relationships among a large set of factors and to construct reliable prediction models based on the given input data. To predict LOS of patients, Wrenn et al. [15] proposed an artificial neural network based prediction model for an Emergency Department. Rowan et al. [13] proposed to predict LOS of cardiac patients using artificial neural networks based on preoperative and initial postoperative factors. Azari et al. [24] proposed a multitiered data mining approach for predicting LOS. They used clustering technique to create a training dataset to train several classification algorithms for prediction of LOS. Classification algorithms were also used by Hachesu et al. [5], and Jiang et al. [14] for predicting LOS. They have demonstrated multiple classification algorithms (decision tree, support vector machines, logistic regression) with varied level of accuracy. Most of the previous works emphasized on the use of novel or hybrid classification algorithms or complex ensemble models [12], [14]. However, the performance of any prediction model depends on the number and type of its inputs variables as well [25]–[27]. Thus, the selection of appropriate input variables or features is critical to the performance of any prediction model. In this work, we proposed to solve the LOS prediction problem using a novel approach combining a feature selection step for optimum mix of input features with a prediction model development step using classification algorithms. Our aim is to provide an early LOS estimation for new patients arriving at a stroke unit.

EXISTING SYSTEM: We categorized data values and derived new fields from existing data in the following features: ejection fraction, diastolic blood pressure, systolic blood pressure, smoking, triglyceride, low-density lipoprotein, high-density lipoprotein, hemoglobin, serum cholesterol, and fasting blood sugar. These features were changed to categorical attributes for analysis and to obtain bad results.

DISADVANTAGES:  These features were changed to categorical attributes for analysis and to obtain bad results. 

PROPOSED SYSTEM: In this work, we proposed to solve the LOS prediction problem using a novel approach combining a feature selection step for optimum mix of input features with a prediction model development step using classification algorithms. Our aim is to provide an early LOS estimation for new patients arriving at a stroke unit. proposed and implemented a software package demonstrating that artificial neural networks could be used as an effective LOS stratification instrument in postoperative cardiac patients

ADVANTAGES:  They proposed using the HR model to predict the mean LOS of stroke patients.  The largest advantage of a registry is the ability to prospectively add patients, while allowing investigators to go back and collect information retrospectively if needed

SYSTEM CONFIGURATION:

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Pentium –IV

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HTML, Java, Jsp.

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JavaScript.

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LITERATURE SURVEY: TITLE: The cost of cerebral ischaemia. ABSTRACT: Cerebral ischaemia is a major cause of disability and death globally and has a profoundly negative impact on the individuals it affects, those that care for them and society as a whole. The most common and familiar manifestation is stroke, 85% of which are ischaemic and which is the second leading cause of death and most common cause of complex chronic disability worldwide. Stroke survivors often suffer from long-term neurological disabilities significantly reducing their ability to integrate effectively in society with all the financial and social consequences that this implies. These difficulties cascade to their next of kin who often become caregivers and are thus indirectly burdened. A more insidious consequence of cerebral ischaemia is progressive cognitive impairment causing dementia which although less abrupt is also associated with a significant long-term disability. Globally cerebrovascular diseases are responsible for 5.4 million deaths every year Approximately 3% of total healthcare expenditure is attributable to cerebral ischaemia with cerebrovascular diseases costing EU healthcare systems 21 billion euro in 2003. The cost to the wider economy (including informal care and lost productivity) is even greater with stroke costing the UK 7-8 billion pound in 2005 and the US $62.7 billion in 2007. Cerebrovascular disease cost the EU 34 billion euro in 2003. From 2005 to 2050 the anticipated cost of stroke to the US economy is estimated at $2.2 trillion. Given the global scale of the problem and the enormous associated costs it is clear that there is an urgent need for advances in the prevention of cerebral ischaemia and its consequences. Such developments would

result in profound benefits for both individuals and their wider societies and address one of the world's most pre-eminent public health issues YEAR OF PUBLICATION:2008 AUTHOR NAME: Flynn RW1, MacWalter RS, Doney AS.

TITLE: Mitochondria, Oxidative Metabolism And Cell Death In Stroke. ABSTRACT: Stroke most commonly results from occlusion of a major artery in the brain and typically leads to the death of all cells within the affected tissue. Mitochondria are centrally involved in the development of this tissue injury due to modifications of their major role in supplying ATP and to changes in their properties that can contribute to the development of apoptotic and necrotic cell death. In animal models of stroke, the limited availability of glucose and oxygen directly impairs oxidative metabolism in severely ischemic regions of the affected tissue and leads to rapid changes in ATP and other energy-related metabolites. In the less-severely ischemic "penumbral" tissue, more moderate alterations develop in these metabolites, associated with near normal glucose use but impaired oxidative metabolism. This tissue remains potentially salvageable for at least the first few hours following stroke onset. Early restoration of blood flow can result in substantial recovery of energy-related metabolites throughout the affected tissue. However, glucose oxidation is markedly decreased due both to lower energy requirements in the post-ischemic tissue and limitations on the mitochondrial oxidation of pyruvate. A secondary deterioration of mitochondrial function subsequently develops that may contribute to progression to cell loss. Mitochondrial release of multiple apoptogenic proteins has been identified in ischemic and post-ischemic brain, mostly in neurons. Pharmacological interventions and genetic modifications in rodent models strongly implicate caspase-dependent and caspase-independent apoptosis and the mitochondrial permeability transition as important contributors to tissue damage, particularly when induced by short periods of temporary focal ischemia. YEAR OF PASSING:2012

AUTHOR NAME: Sims NR1, Muyderman H. TITLE: preliminary data

ABSTRACT: This report presents preliminary U.S. data on deaths, death rates, life expectancy, leading causes of death, and infant mortality for 2008 by selected characteristics such as age, sex, race, and Hispanic origin. Methods-Data in this report are based on death records comprising more than 99 percent of the demographic and medical files for all deaths in the United States in 2008. The records are weighted to independent control counts for 2008. For certain causes of death such as unintentional injuries, homicides, suicides, drug-induced deaths, and sudden infant death syndrome, preliminary and final data may differ because of the truncated nature of the preliminary file. Comparisons are made with 2007 final data. Results-The age-adjusted death rate decreased from 760.2 deaths per 100,000 population in 2007 to 758.7 deaths per 100,000 population in 2008. From 2007 to 2008, age-adjusted death rates decreased significantly for 6 of the 15 leading causes of death: Diseases of heart, Malignant neoplasms, Cerebrovascular diseases, Accidents (unintentional injuries), Diabetes mellitus, andAssault (homicide). From 2007 to 2008, age-adjusted death rates increased significantly for 6 of the 15 leading causes of death: Chronic lower respiratory diseases; Alzheimer's disease; Influenza and pneumonia; Nephritis, nephrotic syndrome and nephrosis; Intentional self-harm (suicide); and Essential hypertension and hypertensive renal disease. YEAR OF PUBLISHING:2008 AUTHOR NAME: Miniño AM, Xu J, Kochanek KD.

TITLE: Predicting length of stay, functional outcome, and aftercare in the rehabilitation of stroke patients. ABSTRACT: Research in recent years has revealed factors that are important predictors of physical and functional rehabilitation: demographic variables, visual and perceptual impairments, and psychological and cognitive factors. However, there is a remaining uncertainty about prediction of outcome and a need to clinically apply research findings. This study was designed to identify the relative importance of medical, functional, demographic, and cognitive factors in predicting length of stay in rehabilitation, functional outcome, and recommendations for post discharge continuation of services. YEAR OF PUBLICATION:2002 AUTHOR NAME: Galski T1, Bruno RL, Zorowitz R, Walker J.

TITLE: Use of data mining techniques to determine and predict length of stay of cardiac patients. ABSTRACT: Predicting the length of stay (LOS) of patients in a hospital is important in providing them with better services and higher satisfaction, as well as helping the hospital management plan and managing hospital resources as meticulously as possible. We propose applying data mining techniques to extract useful knowledge and draw an accurate model to predict the LOS of heart patients. YEAR OF PUBLICATION:2013 AUTHOR NAME: Hachesu PR1, Ahmadi M, Alizadeh S, Sadoughi F.

TITLE: Elderly patients in acute medical wards: factors predicting length of stay in hospital. ABSTRACT: A prospective study of 419 patients aged 70 and over admitted to acute medical wards was carried out by medical staff from a geriatric unit. Data, including presenting problem, housing, social support, mental state, continence, and degree of independence before and after admission, were recorded. Of the 419 patients, 143 remained in hospital after 14 days and 65 after 28 days. The major factors associated with prolonged stay in hospital included advanced age, stroke, confusion and falls as reasons for admission to hospital, incontinence, and loss of independence for everyday activities. Social circumstances did not predict length of stay. Although these factors are interrelated, the most important influence on length of stay was the medical reason for admission. Early contact with the geriatric medical unit in these patients may speed up the recovery or result in more appropriate placement. YEAR OF PUBLICATION:2000 AUTHOR NAME: Maguire PA, Taylor IC, Stout RW.

TITLE: Predicting length of stay in an acute psychiatric hospital. ABSTRACT: Multivariate statistical methods were used to identify patient-related variables that predicted length of stay in a single psychiatric facility. The study investigated whether these variables remained stable over time and could be used to provide individual physicians with data on length of stay adjusted for differences in clinical caseloads and to detect trends in the physicians' practice patterns. YEAR OF PUBLICATION:1999 AUTHOR NAME: Huntley DA1, Cho DW, Christman J, Csernansky JG.

TITLE: Data mining in healthcare and biomedicine: a survey of the literature. ABSTRACT: As a new concept that emerged in the middle of 1990's, data mining can help researchers gain both novel and deep insights and can facilitate unprecedented understanding of large biomedical datasets. Data mining can uncover new biomedical and healthcare knowledge for clinical and administrative decision making as well as generate scientific hypotheses from large experimental data, clinical databases, and/or biomedical literature. This review first introduces data mining in general (e.g., the background, definition, and process of data mining), discusses the major differences between statistics and data mining and then speaks to the uniqueness of data mining in the biomedical and healthcare fields. A brief summarization of various data mining algorithms used for classification, clustering, and association as well as their respective advantages and drawbacks is also presented. Suggested guidelines on how to use data mining algorithms in each area of classification, clustering, and association are offered along with three examples of how data mining has been used in the healthcare industry. Given the successful application of data mining by health related organizations that has helped to predict health insurance fraud and under-diagnosed patients, and identify and classify at-risk people in terms of health with the goal of reducing healthcare cost, we introduce how data mining technologies (in each area of classification, clustering, and association) have been used for a multitude of purposes, including research in the biomedical and healthcare fields. A discussion of the technologies available to enable the prediction of healthcare costs (including length of hospital stay), disease diagnosis and prognosis, and the discovery of hidden biomedical and healthcare patterns from related databases is offered along with a discussion of the use of data mining to discover such relationships as those between health

conditions and a disease, relationships among diseases, and relationships among drugs. The article concludes with a discussion of the problems that hamper the clinical use of data mining by health professionals. YEAR OF PUBLICATION:2011 AUTHOR NAME: Yoo I1, Alafaireet P, Marinov M, Pena-Hernandez K, Gopidi R, Chang JF, Hua L.

TITLE: Uniqueness of medical data mining ABSTRACT: This article addresses the special features of data mining with medical data. Researchers in other fields may not be aware of the particular constraints and difficulties of the privacy-sensitive, heterogeneous, but voluminous data of medicine. Ethical and legal aspects of medical data mining are discussed, including data ownership, fear of lawsuits, expected benefits, and special administrative issues. The mathematical understanding of estimation and hypothesis formation in medical data may be fundamentally different than those from other data collection activities. Medicine is primarily directed at patient-care activity, and only secondarily as a research resource; almost the only justification for collecting medical data is to benefit the individual patient. Finally, medical data have a special status based upon their applicability to all people; their urgency (including life-ordeath); and a moral obligation to be used for beneficial purposes YEAR OF PUBLICATION:2002 AUTHOR NAME: Krzysztof J. Cios

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MODULAR:  PATIENT MODULE  HOSPITAL MODULE PATIENT MODULE:    

LOGIN REGISTER REQUEST LOGOUT

LOGIN: The login module is the very first and the most common module in all applications. In the suggested system only registered users will be allowed to login the system the unauthorized users will be unable to login. Registered users with their username and password only being correct will moved on to the next page. Or else they will be unable to login. REGISTER: This module is User Registration; all the new users have to register. Each user is given a unique password with their user name. To access their account they have to give their valid username and password i.e. authentication and security is provided for their account. REQUEST: Request for Offer is an open and competitive purchasing process whereby an organization requests the submission of offers in response to .Request is waiting for the response.

LOGOUT: Loging out means to end access to a computer system or a website. Logging out informs the computer or website that the current user wishes to end the login session. Log out is also known as log off, sign off or sign out.

HOSPITAL MODULE:     

LOGIN ACCEPT THE REQUEST VIEW PATIENT HISTORY BED ALLOCATION FOR THE PATIENT LOGOUT

LOGIN: The login module is the very first and the most common module in all applications. In the suggested system only registered users will be allowed to login the system the unauthorized users will be unable to login. Registered users with their username and password only being correct will moved on to the next page. Or else they will be unable to login. ACCEPT THE REQUEST The Accept request-header field can be used to specify certain media types which are acceptable for the response VIEW PATIENT HISTORY: It also contains information relating to the patient's Primary and Secondary Addresses, Emergency Contacts, Referrals, and Family. Social. The Social

section of a patient's clinical record is for keeping record of the patient's social habits and lifestyle choices. BED ALLOTMENT:

Bed management is the allocation and provision of beds, especially in a hospital where beds in specialist wards are a scarce resource.The "bed" in this context represents not simply a place for the patient to sleep, but the services that go with being cared for by the medical facility: admission processing, physician time, nursing care, necessary diagnostic work, appropriate treatment, and so forth. LOGOUT Loging out means to end access to a computer system or a website. Logging out informs the computer or website that the current user wishes to end the login session. Log out is also known as log off, sign off

LANGUAGE SPECIFICATION: Java Technology Java technology is both a programming language and a platform. The Java Programming Language The Java programming language is a high-level language that can be characterized by all of the following buzzwords:  Simple  Architecture neutral  Object oriented  Portable  Distributed  High performance  Interpreted  Multithreaded  Robust  Dynamic  Secure

With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.

You can think of Java bytecodes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java bytecodes help make “write once, run anywhere” possible. You can compile your program into bytecodes on any platform that has a Java compiler. The bytecodes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.

The Java Platform A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardwarebased platforms.The Java platform has two components: 

The Java Virtual Machine (Java VM)

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The Java Application Programming Interface (Java API)

You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms. The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do?, highlights what functionality some of the packages in the Java API provide. The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.

Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can

be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time bytecode compilers can bring performance close to that of native code without threatening portability.

What Can Java Technology Do? The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser. However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.

An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet. A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.

How does the API support all these kinds of programs? It does so with packages of software components that provide a wide range of functionality. Every full implementation of the Java platform gives you the following features:

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The essentials: Objects, strings, threads, numbers, input and output, data structures, system properties, date and time, and so on.

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Applets: The set of conventions used by applets.

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Networking: URLs, TCP (Transmission Control Protocol), UDP (User Data gram Protocol) sockets, and IP (Internet Protocol) addresses.

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Internationalization: Help for writing programs that can be localized for users worldwide. Programs can automatically adapt to specific locales and be displayed in the appropriate language.

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Security: Both low level and high level, including electronic signatures, public and private key management, access control, and certificates.

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Software components: Known as JavaBeansTM, can plug into existing component architectures.

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Object serialization: Allows lightweight persistence and communication via Remote Method Invocation (RMI).

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Java Database Connectivity (JDBCTM): Provides uniform access to a wide range of relational databases.

The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.

How Will Java Technology Change My Life? We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following: 

Get started quickly: Although the Java programming language is a powerful object-oriented language, it’s easy to learn, especially for programmers already familiar with C or C++.

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Write less code: Comparisons of program metrics (class counts, method counts, and so on) suggest that a program written in the Java programming language can be four times smaller than the same program in C++.

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Write better code: The Java programming language encourages good coding practices, and its garbage collection helps you avoid memory leaks. Its object orientation, its JavaBeans component architecture, and its wideranging, easily extendible API let you reuse other people’s tested code and introduce fewer bugs.

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Develop programs more quickly: Your development time may be as much as twice as fast versus writing the same program in C++. Why? You write fewer lines of code and it is a simpler programming language than C++.

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Avoid platform dependencies with 100% Pure Java: You can keep your program portable by avoiding the use of libraries written in other languages. The 100% Pure Java TM Product Certification Program has a repository of historical process manuals, white papers, brochures, and similar materials online.

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Write once, run anywhere: Because 100% Pure Java programs are compiled into machine-independent byte codes, they run consistently on any Java platform.

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Distribute software more easily: You can upgrade applets easily from a central server. Applets take advantage of the feature of allowing new classes to be loaded “on the fly,” without recompiling the entire program.

ODBC Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.

Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.

The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data Sources. From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.

The advantages of this scheme are so numerous that you are probably thinking there must be some catch. The only disadvantage of ODBC is that it isn’t as efficient as talking directly to the native database interface. ODBC has had many detractors make the charge that it is too slow. Microsoft has always claimed that the critical factor in performance is the quality of the driver software that is used. In our humble opinion, this is true. The availability of good ODBC drivers has improved a great deal recently. And anyway, the criticism about performance is somewhat analogous to those who said that compilers would never match the speed of pure assembly language. Maybe not, but the compiler (or ODBC) gives you the opportunity to write cleaner programs, which means you finish sooner. Meanwhile, computers get faster every year.

JDBC In an effort to set an independent database standard API for Java, Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMS. This consistent interface is achieved through the use of “plugin” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on. To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution. JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.

The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.

JDBC Goals Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java. The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:

1. SQL Level API The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.

2. SQL Conformance SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the

connectivity module to handle non-standard functionality in a manner that is suitable for its users. 3. JDBC must be implemental on top of common database interfaces The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa. 4. Provide a Java interface that is consistent with the rest of the Java system Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system. 5. Keep it simple This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API. 6. Use strong, static typing wherever possible Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.

7. Keep the common cases simple Because more often than not, the usual SQL calls used by the programmer are simple SELECT’s, INSERT’s, DELETE’s and UPDATE’s, these queries should be simple to perform with JDBC. However, more complex SQL statements should also be possible. Finally we decided to proceed the implementation using Java Networking. And for dynamically updating the cache table we go for MS Access database.

simple

architecture-neutral

object-oriented portable Distributed high-performance interpreted Java is also unusual in that each java program is both compiled and interpreted. With a compile you translate a java program into an intermediate language called java byte codes the platform-independent code instruction is passed and run on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works. Interpreter

Java Program

Compilers

My Program

You can think of java byte codes as the machine code instructions for the java virtual machine (java vm). Every java interpreter, whether it’s a java development tool or a web browser that can run java applets, is an implementation of the java vm. The java vm can also be implemented in hardware. Java byte codes help make “write once, run anywhere” possible. You can compile your java program into byte codes on my platform that has a java compiler. The

byte codes can then be run any implementation of the java vm. For example, the same java program can run windows nt, solaris, and macintosh.

SYSTEM TESTING:

The purpose of testing is to discover errors. Testing is the process of trying to discover every conceivable fault or weakness in a work product. It provides a way to check the functionality of components, sub assemblies, assemblies and/or a finished product It is the process of exercising software with the intent of ensuring that the Software system meets its requirements and user expectations and does not fail in an unacceptable manner. There are various types of test. Each test type addresses a specific testing requirement.

TYPES OF TESTS

Unit testing

Unit testing involves the design of test cases that validate that the internal program logic is functioning properly, and that program inputs produce valid outputs. All decision branches and internal code flow should be validated. It is the testing of individual software units of the application .it is done after the completion of an individual unit before integration. This is a structural testing, that relies on knowledge of its construction and is invasive. Unit tests perform basic tests at component level and test a specific business process, application, and/or system configuration. Unit tests ensure that each unique path of a business process performs accurately to the documented specifications and contains clearly defined inputs and expected results.

Integration testing

Integration tests are designed to test integrated software components to determine if they actually run as one program. Testing is event driven and is more concerned with the basic outcome of screens or fields. Integration tests demonstrate that although the components were individually satisfaction, as shown by successfully unit testing, the combination of components is correct and consistent. Integration testing is specifically aimed at

exposing the problems that

arise from the combination of components.

Functional test

Functional tests provide a systematic demonstration that functions tested are available as specified by the business and technical requirements, system documentation, and user manuals. Functional testing is centered on the following items: Valid Input

: identified classes of valid input must be accepted.

Invalid Input

: identified classes of invalid input must be rejected.

Functions

: identified functions must be exercised.

Output

: identified classes of application outputs must be exercised.

Systems/Procedures: interfacing systems or procedures must be invoked. Organization and preparation of functional tests is focused on requirements, key functions, or special test cases. In addition, systematic coverage pertaining to identify Business process flows; data fields, predefined processes, and successive processes must be considered for testing. Before functional testing is complete, additional tests are identified and the effective value of current tests is determined.

System Test

System testing ensures that the entire integrated software system meets requirements. It tests a configuration to ensure known and predictable results. An example of system testing is the configuration oriented system integration test. System testing is based on process descriptions and flows, emphasizing pre-driven process links and integration points.

White Box Testing

White Box Testing is a testing in which in which the software tester has knowledge of the inner workings, structure and language of the software, or at least its purpose. It is purpose. It is used to test areas that cannot be reached from a black box level.

Black Box Testing

Black Box Testing is testing the software without any knowledge of the inner workings, structure or language of the module being tested. Black box tests, as most other kinds of tests, must be written from a definitive source document, such as specification or requirements document, such as specification or requirements document. It is a testing in which the software under test is treated, as a black box .you cannot “see” into it. The test provides inputs and responds to outputs without considering how the software works.

Unit Testing: Unit testing is usually conducted as part of a combined code and unit test phase of the software lifecycle, although it is not uncommon for coding and unit testing to be conducted as two distinct phases.

Test strategy and approach

Field testing will be performed manually and functional tests will be written in detail. Test objectives 

All field entries must work properly.



Pages must be activated from the identified link.



The entry screen, messages and responses must not be delayed.

Features to be tested 

Verify that the entries are of the correct format



No duplicate entries should be allowed



All links should take the user to the correct page.

Integration Testing Software integration testing is the incremental integration testing of two or more integrated software components on a single platform to produce failures caused by interface defects. The task of the integration test is to check that components or software applications, e.g. components in a software system or – one step up – software applications at the company level – interact without error. Test Results: All the test cases mentioned above passed successfully. No defects encountered. Acceptance Testing

User Acceptance Testing is a critical phase of any project and requires significant participation by the end user. It also ensures that the system meets the functional requirements. Test Results: All the test cases mentioned above passed successfully. No defects encountered.

CONCLUSION: This study introduces an approach for early prediction of LOS of stroke patients arriving at a stroke unit. The approach involves a feature selection step based on information gain and a prediction model development step using J48 decision tree or Bayesian network. Prediction results were compared between the two models. The performance of the Bayesian network based model was better (accuracy 81.28%) as opposed to the performance of the J48 based prediction model (accuracy 77.1%). A partial representation of the J48 based model and the Bayesian network based model are exhibited in Fig. 3 and Fig. 4 respectively. The study faced some limitations in terms of data availability and the quality of the data. More than 50% of the collected records were discarded due to incompleteness. Nevertheless, the performance of the proposed prediction model is quite promising. In future studies, the proposed approach will be tested on larger datasets. Another important area for future research is to extend the proposed approach to predict other attributes such as the Stroke Level of the patients

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