Comparison of Machine Learning Algorithms for Liver Disease Classification

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Published: Jun 25, 2025
Keywords:
liver disease, classification, machine learning, ILPD dataset

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Nur Futri Ayu Jelita
Department of Information Systems, Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif Kasim, Riau, Indonesia
Nayla Husna Ryanda
Department of Information Systems, Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif Kasim, Riau, Indonesia
Fatimah Fhingkan Agustina
Department of Information Systems, Faculty of Computer Science, Universitas Muhammadiyah Riau, Indonesia

Abstract

Liver disease is a serious medical condition often diagnosed late due to the lack of early symptoms and limited access to fast and affordable diagnostic technologies. This study utilized the Indian Liver Patient Dataset (ILPD) to develop a machine learning- based liver disease prediction model. The research process included data collection, preprocessing, model building, and performance comparison of various algorithms, such as Naive Bayes, K-Nearest Neighbor (KNN), Random Forest, Logistic Regression, Support Vector Machine (SVM), and Neural Network. The evaluation results revealed that Logistic Regression achieved the best performance with an accuracy of 72.00%, precision of 91.80%, and recall of 74.70%, offering a balance between accurate detection and minimal diagnostic errors. This study concludes that Logistic Regression is the most effective algorithm for liver disease prediction, supporting early detection and medical decision-making.

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Vol. 1 No. 1 (2025): Juni 2025
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