Enhancing Liver Disease Diagnosis with Machine Learning: An Empirical Evaluation of Algorithms on the Indian Liver Patient Dataset - Thesis

Aug 5, 2026

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Liver disease is a critical global health concern with significant morbidity and mortality. Early and accurate diagnosis is essential for effective treatment and management. This thesis uses machine learning to improve liver disease diagnosis with the Indian Liver Patient Dataset (ILPD). It performs data preprocessing and exploratory data analysis to ensure integrity and extract initial insights, then applies and evaluates interpretable classifiers, including decision trees, neural networks, K-nearest neighbors, and gradient boosting, using accuracy, precision, recall, and F1-score after SMOTE. Cross-validation and hyperparameter tuning support generalizable results.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages24

Reading Time1 h 0 min

AccessFree to download

Acknowledgement

Abstract

Introduction

Related Work

Dataset

Dataset for proposed method

Dataset used for fair comparison

Exploratory Data Analysis

Methodology

Data Preprocessing

Over Sampling

Cross-Validation

Algorithms Used

Model Evaluation

Hyperparameter Tuning

Results And Analysis

Analysis

Conclusion

Future Work

References

CALIFORNIA STATE UNIVERSITY SAN MARCOS THESIS SIGNATURE PAGE THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE MASTER OF SCIENCE IN COMPUTER SCIENCE Thesis TITLE: Enhancing Liver Disease Diagnosis with Machine Learning: An Empirical Evaluation of Algorithms on the Indian Liver Patient Dataset AUTHOR: Yashaswini Prakash DATE OF SUCCESSFUL DEFENSE: THE THESIS HAS BEEN ACCEPTED BY THE THESIS COMMITTEE IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCSIENCE. Dr. Ahmed Hadaegh THESIS COMMITTEE CHAIR Dr. Sreedevi Gutta THESIS COMMITTEE MEMBER SIGNATURE DATE Enhancing Liver Disease Diagnosis with Machine Learning: An Empirical Evaluation of Algorithms on the Indian Liver Patient Dataset By: Yashaswini Prakash Date: May 2024…. Table of Contents Table of Contents .............................................................................................................................. 3 Acknowledgement ............................................................................................................................ 4 Abstract.............................................................................................................................................. 5 1. Introduction .............................................................................................................................. 6 2. Related Work ............................................................................................................................ 6 3. Dataset....................................................................................................................................... 8 Dataset for proposed method:....................................................................................................... 8 Dataset used to compare the proposed methodology of tuned methods with the prior work for fair comparison:....................................................................................................................... 8 3.1. Features: ........................................................................................................................... 8 4. Exploratory Data Analysis ....................................................................................................... 9 5. Methodology........................................................................................................................... 12 5.1. Data Preprocessing: ....................................................................................................... 13 5.2. Over Sampling ............................................................................................................... 14 5.3. Cross-Validation:............................................................................................................ 15 5.4. Algorithms Used: ........................................................................................................... 15 5.5. Model Evaluation:.......................................................................................................... 16 5.6. Hyperparameter Tuning:................................................................................................ 16 6. Results And Analysis ............................................................................................................. 17 7. Analysis: ................................................................................................................................. 19 8. Conclusion .............................................................................................................................. 20 9. Future Work ............................................................................................................................ 21 References .................................................................

FAQs

What dataset is used to train and evaluate the machine learning models?

The thesis uses the Indian Liver Patient Dataset (ILPD). It also applies SMOTE to address class balance before evaluating performance.

Which machine learning algorithms are evaluated for liver disease diagnosis?

The study evaluates decision trees, neural networks, K-nearest neighbors, and gradient boosting classifiers, with emphasis on interpretability for medical diagnostic use.

How are model performance and generalization assessed?

Performance is measured using accuracy, precision, recall, and particularly the F1-score. Hyperparameter tuning and cross-validation are used to improve generalizability.

Aug 5, 2026

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