Report.docx

A PROJECT REPORT on

“DRUG AND MATERIALS PREDICTION”

Submitted to

KIIT Deemed to be University

In Partial Fulfillment of the Requirement for the Award of BACHELOR’S DEGREE IN COMPUTER SCIENCE & ENGINEERING BY ANKUSH PAL 1505365 ANKIT RANJAN 1505364 ASHUTOSH DASH 1505374 SUCCESS PRADHAN 1505431 ANIMESH BISWAL 1505362

UNDER THE GUIDANCE OF PROF. SARITA TRIPATHY

KIIT Deemed to be University School of Computer Engineering Bhubaneswar, ODISHA 751024

CERTIFICATE This is certify that the project entitled

“DRUG AND MATERIALS PREDICTION“ submitted by ANKUSH PAL 1505362 ANKIT RANJAN 1505364 ASHUTOSH DASH 1505374 SUCCESS PRADHAN 1505431 ANIMESH BISWAL 1505362 is a record of bonafide work carried out by them, in the partial fulfillment of the requirement for the award of Degree of Bachelor of Engineering (Computer Science & Engineering OR Information Technology) at KIIT Deemed to be university, Bhubaneswar. This work is done during year 2018-2019, under our guidance. Date: 3 / 3 / 19

PROF. SARITA TIPATHY Project Guide

Acknowledgements We are profoundly grateful to Prof. SARITA TRIPATHY for his expert guidance and continuous encouragement throughout to see that this project rights its target since its commencement to its completion. ........… ANKUSH PAL ANKIT RANJAN ASHUTOSH DASH SUCCESS PRADHAN ANIMESH BISWAL

ABSTRACT A hospital has given its data of years containing patient unique ID’s, patient gender, patient age, team (the department in which his checkup has been carried out), number of days he stayed in the hospital and finally on which day what was issued to him. Now, the task is to predict, if a patient gets admitted to the hospital and the team which is taking care of the patient is known then we should be able to predict what is going to be issued to that patient.

Contents

CHAPTER 1: Business Scenario .......................................................................................5 CHAPTER 2:Problem Statement .....................................................................................6 CHAPTER 3:Introduction to Machine Learning .......................................................... 7 CHAPTER 4:Machine Learning Algorithms ..............................................................8-9

CHAPTER 5: Neural Network ............................................................................... 10-13 CHAPTER 6:R Programming.…………………………………………………………..14 CHAPTER 7:Long Data to Wide Data Conversion in R ....................................... 15 CHAPTER 8: Neuralnet Library in R..................................................................... 16 CHAPTER 9:Training Modal & Making Prediction............................................. 17 - 21 SUMMARY AND CONCLUSION REFERENCE

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CHAPTER 1 Business Scenario Ubq technologies have various hospitals as their client. Hospitals have data of various year which is sufficient enough to make a prediction or find out patterns of repetition among the drugs or materials issued to a patient. Now, they want make the prediction of all those items that are going to be issued to a particular on a particular day . Ubq receives denormalized data in excel from various hospitals, normalizes it with SQL, creates required relationships. The raw data is converted in meaningful set of information in this process. Analysis is performed on top of this.

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CHAPTER 2 Problem Statement A hospital has given its data of years containing patient unique ID’s, patient gender, patient age, team (the department in which his checkup has been carried out), number of days he stayed in the hospital and finally on which day what was issued to him. Now, the task is to predict, if a patient gets admitted to the hospital and the team which is taking care of the patient is known then we should be able to predict what is going to be issued to that patient.

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CHAPTER 3 Introduction to Machine Learning Machine Learning is an idea to learn from examples and experience, without being explicitly programmed. Instead of writing code, you feed data to the generic algorithm, and it builds logic based on the data given. “A computer program is said to learn from experience E with some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.” . Need of Machine Learning Machine Learning is a field which is raised out of Artificial Intelligence(AI). Applying AI, we wanted to build better and intelligent machines. But except for few mere tasks such as finding the shortest path between point A and B, we were unable to program more complex and constantly evolving challenges.There was a realisation that the only way to be able to achieve this task was to let machine learn from itself. This sounds similar to a child learning from its self. So machine learning was developed as a new capability for computers. And now machine learning is present in so many segments of technology, that we don’t even realise it while using it. Finding patterns in data on planet earth is possible only for human brains. The data being very massive, the time taken to compute is increased, and this is where Machine Learning comes into action, to help people with large data in minimum time. If big data and cloud computing are gaining importance for their contributions, machine learning as technology helps analyse those big chunks of data, easing the task of data scientists in an automated process and gaining equal importance and recognition. The techniques we use for data mining have been around for many years, but they were not effective as they did not have the competitive power to run the algorithms.

If you run deep learning with access to better data, the output we get will lead to dramatic breakthroughs which is machine learning.

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CHAPTER 4 Machine Learning Algorithms Types of Machine Learning Algorithms :1.Supervised Learning 1. How it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.

2. Unsupervised Learning How it works: In this algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. Examples of Unsupervised Learning: Apriori algorithm, K-means.

3. Reinforcement Learning: How it works: Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Example of Reinforcement Learning: Markov Decision Process

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List of Common Machine Learning Algorithms : Here is the list of commonly used machine learning algorithms. These algorithms can be applied to almost any data problem: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Linear Regression Logistic Regression Decision Tree SVM Naive Bayes kNN K-Means Random Forest Dimensionality Reduction Algorothm Gradient Boosting Algorithm Neural Network

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CHAPTER 5 Neural Network The simplest definition of a neural network, more properly referred to as an 'artificial' neural network (ANN), is provided by the inventor of one of the first neurocomputers, Dr. Robert Hecht-Nielsen. He defines a neural network as: "...a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs. In "Neural Network Primer: Part I" by Maureen Caudill, AI Expert, Feb. 1989 ANNs are processing devices (algorithms or actual hardware) that are loosely modeled after the neuronal structure of the mamalian cerebral cortex but on much smaller scales. A large ANN might have hundreds or thousands of processor units, whereas a mamalian brain has billions of neurons with a corresponding increase in magnitude of their overall interaction and emergent behavior. Although ANN researchers are generally not concerned with whether their networks accurately resemble biological systems, some have. For example, researchers have accurately simulated the function of the retina and modeled the eye rather well. Although the mathematics involved with neural networking is not a trivial matter, a user can rather easily gain at least an operational understanding of their structure and function. The Basics of Neural Networks

Neural neworks are typically organized in layers. Layers are made up of a number of interconnected 'nodes' which contain an 'activation function'. Patterns are presented to the network via the 'input layer', which communicates to one or more 'hidden layers' where the actual processing is done via a system of weighted 'connections'. The hidden layers then link to an 'output layer' where the answer is output as shown in the graphic below.

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Most ANNs contain some form of 'learning rule' which modifies the weights of the connections according to the input patterns that it is presented with. In a sense, ANNs learn by example as do their biological counterparts; a child learns to recognize dogs from examples of dogs. Although there are many different kinds of learning rules used by neural networks, this demonstration is concerned only with one; the delta rule. The delta rule is often utilized by the most common class of ANNs called 'backpropagational neural networks' (BPNNs). Backpropagation is an abbreviation for the backwards propagation of error. With the delta rule, as with other types of backpropagation, 'learning' is a supervised process that occurs with each cycle or 'epoch' (i.e. each time the network is presented with a new input pattern) through a forward activation flow of outputs, and the backwards error propagation of weight adjustments. More simply, when a neural network is initially presented with a pattern it makes a random 'guess' as to what it might be. It then sees how far its answer was from the actual one and makes an appropriate adjustment to its connection weights. More graphically, the process looks something like this:

Note also, that within each hidden layer node is a sigmoidal activation function which polarizes network activity and helps it to stablize. Backpropagation performs a gradient descent within the solution's vector space towards a 'global minimum' along the steepest vector of the error surface. The global minimum is that theoretical solution with the lowest possible error. The error surface itself is a hyperparaboloid but is seldom 'smooth' as is depicted in the graphic below. Indeed, in most problems, the solution space is quite irregular with numerous 'pits' and 'hills' which may cause the network to settle down in a 'local minum' which is not the best overall solution.

Since the nature of the error space can not be known a prioi, neural network analysis often requires a large number of individual runs to determine the best solution. Most learning rules have built-in mathematical terms to assist in this process which control the 'speed' (Beta-coefficient) and the 'momentum' of the learning. The speed of learning is actually the rate of convergence between the current solution and the global minimum. Momentum helps the network to overcome obstacles (local minima) in the error surface and settle down at or near the global miniumum.

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Chapter 6 System Testing WRITE HERE.

6.1 T e s t I D T 0 1 T 0 2 T 0 3

Test Cases and Test Results Test Case Title

Test Condition

System Behavior

Expected Result

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Note: Testing should be performed manually

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Chapter 7 Project Planning 7.1

SECTION 1

WRITE HERE.

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Chapter 8 Implementation WRITE HERE, PARAGRAPH 1. WRITE HERE, PARAGRAPH 2.

Figure 8.1: IMAGE CAPTION

1

PASTE YOUR CODE HERE

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Chapter 9 Screenshots of Project 9.1

SECTION NAME

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Chapter 10 Conclusion and Future Scope 10.1

Conclusion

WRITE HERE.

10.2

Future Scope

WRITE HERE. • ITEM 1 • ITEM 2 • ITEM 3

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References [1] NAME OF IEEE PAPER; NAME OF AUTHORS [2] http://EXAMPLE.com

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