AN EVALUATION OF MACHINE LEARNING APPROACHES FOR PREDICTING CHRONIC KIDNEY DISEASE

AN EVALUATION OF MACHINE LEARNING APPROACHES FOR PREDICTING CHRONIC KIDNEY DISEASE

Abstract

Nowadays, machine learning is the biggest aid in creating models of genuine biological systems that are both instructive and predictive. The area of science where we can get the most data is biology. Due to the ever-increasing number and complexity of biological data, machine learning has become a growing application in biomedical technology. When the kidneys are injured and the wastes cannot be filtered as the kidneys normally can, this condition is known as chronic kidney disease or chronic renal failure. The main causes of CKD include diabetes and high blood pressure. When damage necessitates a kidney transplant, the situation becomes problematic. Early identification is therefore crucial in this case, and I have used some ML to predict CKD. employing a dataset of 400 clinical data to demonstrate approaches. Although I used the majority of machine learning techniques, four of them (Random Forest, XGBoost, Ada Boost, and LGBM Classifier) showed promise, and the performance of this model is the best in predicting CKD using the provided dataset.

Table of Contents

Approval….………………………………………………………………………………………ii

Declaration iii
Acknowledgment iv
Table of contents v
List of Figure vi
List of Table vii
List of Nomenclature viii
Abstract ix

Chapter 1: Introduction ……………………………………………………………………….
1.1 Problem Description ……………………………………………………………………….
1.2 Motivation of the research …………………………………………………………………
1.3 Objective of the research……………………………………………………………………
1.4 Research Design …………………………………………………………………….……..

Chapter 2: Background Study………………………………………………………..……….

Chapter 3: Research Methodology……….…………………………………………….….…
3.1 Dataset Explanation………………………………………………………………………
3.2 Data Pre-processing ……………………………………………………………..….……
3.3 Performance Measure Parameter ………………………………………………..……….
3.4 Ensemble classification algorithm …………………………………………….………..

Chapter 4: Result & Discussion………………………………………………………………..

Chapter 5: Conclusion & Recommendation…………………………………………………

Reference………………………………………………………………………………………

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