Comparative Analysis of Machine Learning Algorithms for Liver Disease Prediction: SVM, Logistic Regression, and Decision Tree

Venugopal Reddy Modhugu *

Fallschurch, Virginia, 22042, USA.

Sivakumar Ponnusamy

Independent Researcher, Richmond, VA, 23233, USA.

*Author to whom correspondence should be addressed.


Abstract

This study compares Support Vector Machine (SVM), Logistic Regression, and Decision Tree algorithms for liver disease prediction using a dataset sourced from Kaggle, comprising 20,000 training records and approximately 1,000 test records. The research evaluates the algorithms based on performance metrics, including accuracy, precision, recall, and F1-score. SVM emerged as the most effective model with an accuracy of 85%, followed by Logistic Regression with 82% and Decision Tree with 79%. The findings underscore the significance of algorithm selection in healthcare applications and highlight SVM's potential for early detection and intervention in liver disease cases, paving the way for improved patient outcomes and healthcare management. Future work will focus on refining the algorithms and validating the results with larger and more diverse datasets to enhance predictive accuracy and robustness further.

Keywords: Liver disease prediction, machine learning algorithms, Support Vector Machine (SVM), logistic regression, decision tree


How to Cite

Modhugu, V. R., & Ponnusamy, S. (2024). Comparative Analysis of Machine Learning Algorithms for Liver Disease Prediction: SVM, Logistic Regression, and Decision Tree. Asian Journal of Research in Computer Science, 17(6), 188–201. https://doi.org/10.9734/ajrcos/2024/v17i6467

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