Hepatitis B prediction using Decision Tree algorithm in python

 

Hepatitis B prediction using Decision Tree algorithm

 

 PROJECT ID: PYTHON13

 

PROJECT NAME: Hepatitis B prediction using Decision Tree algorithm

 

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

Hepatitis B surface antigen (HBsAg) seroclearance during treatment is associated with a better prognosis among patients with chronic hepatitis B (CHB). Significant gaps remain in our understanding on how to predict HBsAg seroclearance accurately and efficiently based on obtainable clinical information. This study aimed to identify the optimal model to predict HBsAg seroclearance. We obtained the laboratory and demographic information for 2,235 patients with CHB from the South China Hepatitis Monitoring and Administration (SCHEMA) cohort. HBsAg seroclearance occurred in 106 patients in total. We developed models based on four algorithms, including the extreme gradient boosting (XGBoost), random forest (RF), decision tree (DCT), and logistic regression (LR). The optimal model was identified by the area under the receiver operating characteristic curve (AUC). The AUCs for XGBoost, RF, DCT, and LR models were 0.891, 0.829, 0.619, and 0.680, respectively, with XGBoost showing the best predictive performance. The variable importance plot of the XGBoost model indicated that the level of HBsAg was of high importance followed by age and the level of hepatitis B virus (HBV) DNA. Machine learning algorithms, especially XGBoost, have appropriate performance in predicting HBsAg seroclearance. The results showed the potential of machine learning algorithms for predicting HBsAg seroclearance utilizing obtainable clinical data.

 

HBV infection remains an urgent public health issue worldwide. Roughly 257 million individuals have been infected with HBV, and more than 350 million patients are living with CHB [1]. It is well documented that HBsAg seroclearance is an important milestone for prognosis during the treatment of CHB [2–4]. The annual incidence of spontaneous HBsAg seroclearance in chronically HBV-infected patients varied from 0.45% to 2.38% worldwide, indicating that HBsAg seroclearance is a rare event [5–10]. Previous studies have suggested a potential association between spontaneous or therapy-induced HBsAg seroclearance and a better prognosis, liver histological improvement, a diminished risk of hepatocellular carcinoma (HCC), and extended survival [11–13]. Therefore, HBsAg seroclearance is an important endpoint achieving a better outcome of antiviral therapy.

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

Conclusion:

In this study, we conducted an evaluation and comparison of four well-known machine learning algorithms by regressing the HBsAg seroclearance status of each patient against laboratory and demographic variables. The results show that machine learning algorithms, especially XGBoost, can predict HBsAg seroclearance with an efficient accuracy. This study also showed a potential of machine learning algorithms being used for clinical outcome predictions.

 

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

Other Information

 

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