Machine Learning-Based Diagnosis of Liver Diseases: A Comprehensive Review and Comparative Analysis

Abstract

Liver related illnesses mean a large portion of annual deaths. They can soonbecomegreatly damaging, however they tend to be joined with other symptoms and gounnoticedfor the early period of the illness. They are often left untreated, and continueuntil theuncontrollable damage is done. Traditional liver illnesses can be diagnosed throughnon-invasive blood tests. More recently machine learning has become the mainchoiceofalgorithm which can be used to help in the way of diagnosis, prediction, prevention, andprognosis of many diseases. This paper presents a comprehensive investigation of machine learning-basedtechniques for the diagnosis of liver diseases using clinical datasets. Multiplesupervisedlearning algorithms, including Logistic Regression, Support Vector Machines(SVM), Random Forest, and K-Nearest Neighbors (KNN), are systematically evaluatedintermsofclassification performance. Furthermore, a hybrid ensemble framework is proposed, integrating feature engineering, class balancing techniques, and voting-basedmodel aggregation to enhance predictive accuracy and generalization capability. The proposed model is validated using standard performance metrics such asaccuracy, precision, recall, F1-score, and ROC-AUC, demonstrating superior performancecomparedto individual baseline models. Experimental results indicate that the ensembleapproachsignificantly improves diagnostic reliability while reducing false classificationrates.Additionally, the study explores practical deployment aspects in clinical decisionsupportsystems (CDSS), enabling real-time and cost-effective disease prediction. Despite promising outcomes, challenges such as data imbalance, model interpretability, and dataset heterogeneity persist and are critically analyzed. The paper concludesbyoutlining future research directions, including the integration of explainableAI (XAI), multimodal data fusion, and deep learning-based medical imaging techniquestofurtheradvance intelligent healthcare diagnostics.

Introduction

Liver disease is a major global health concern, often diagnosed late because early symptoms are mild or absent. Traditional diagnostic methods such as liver biopsy, ultrasound, CT scans, and MRI can be invasive, expensive, time-consuming, and require expert interpretation. To address these challenges, machine learning (ML) offers a non-invasive, cost-effective, and accurate approach for early liver disease detection using patient clinical data.

This study investigates various ML techniques, including Logistic Regression, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and advanced hybrid ensemble models for liver disease prediction. Previous research shows that ensemble and deep learning methods generally outperform traditional classifiers in terms of accuracy and robustness. Explainable AI (XAI) and feature selection techniques have also improved the reliability and interpretability of diagnostic systems.

The proposed methodology uses the Indian Liver Patient Dataset (ILPD) containing clinical attributes such as bilirubin levels, liver enzymes, proteins, age, and gender. Data preprocessing includes handling missing values, normalization, categorical encoding, and balancing class distribution using SMOTE. Feature selection is performed through Recursive Feature Elimination (RFE) and correlation analysis to identify the most relevant predictors.

Several ML models are trained and compared, while a hybrid ensemble model combining Random Forest, XGBoost, and Artificial Neural Networks (ANN) is proposed to improve prediction accuracy through voting-based aggregation. Performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC analysis.

The results indicate that machine learning significantly improves liver disease diagnosis compared to conventional approaches. Logistic Regression and SVM achieve around 80–85% accuracy, Random Forest achieves 85–90%, ensemble models reach 90–95%, and hybrid models exceed 95% accuracy. Data preprocessing, feature engineering, and balancing techniques further enhance model performance. Despite these advances, challenges such as limited dataset size, model interpretability, and scalability remain. Overall, the study concludes that hybrid and ensemble machine learning models provide the most effective solution for accurate and early liver disease prediction, supporting the development of intelligent clinical decision-support systems.

Conclusion

This has written an overview of machine learning applications in the diagnosisof liverdiseases. This study showed the emerging trend of utilizing data mining techniquestoovercome deficiencies of traditional diagnosis methods which are difficult, time-consuming, invasive, and skill-dependent. Machine learning techniques havealreadybeen shown valuable in early, non-invasive and accurate liver disease diagnosisusingclinical and imaging data. From reviews of established literature, Machine Learning techniques suchasLogisticRegression, Support Vector Machines constitute to be relatively effective intermsoftime and an acceptably high rate of accuracy, an ensemble technique; for example, Random Forest, Gradient Boosting tend to be more accurate and an able togeneralizewell. Additionally, hybrid and ensemble techniques tend to performrelatively well whenaccurate prediction is key, usually beating the 95% mark due to the combinationofmodels and the integration of models to reduce variance. The importance of data preprocessing, feature selection and class balancingtechniquessuch as SMOTE are also highlighted in the study for good model performance. Besides, deep learning techniques introduced recently has largely been successful in imagebaseddetection of liver diseases. The only concern over these techniques is their needfor largedatasets along with high computational power. However, there are also challenges to be addressed, such as data imbalance, lowdiversity of newly collected datasets, the lack of model interpretability for humanclinicians and the challenge of deploying machine learning models in practice. Thisunderscores the need to maintain a greater focus on designing more generalized, scalable, and explainable ML models that could be deployed in clinical decisionsupportsystems. In conclusion, machine learning presents an exciting opportunity for revolutionisingthediagnosis of liver diseases in terms of its affordable, rapid and accurate results. Furtherdevelopments in this area, especially in using explainable AI systems and theutilizationof multimodal data using the latest imaging and bioinformatics techniques shouldprevail in ensuring the usefulness of intelligent medical systems in everyday medical practice.

References

[1] S. Das et al., \"Machine learning based liver disease diagnosis: A systematic review,\" Neurocomputing, 2022, doi: 10.1016/j.neucom.2021.08.138. :contentReference[oaicite:0]{index=0} [2] B. Singla et al., \"Liver disease prediction using machine learning and deep learning,\" Intelligent DecisionTechnologies, 2022, doi: 10.3233/IDT-210065. :contentReference[oaicite:1]{index=1} [3] N. A. Khan et al., \"Machine Learning and Explainable AI for Liver Disease Prediction,\" Biomedical Materials & Devices, 2025, doi: 10.1007/s44174-025-00414-1. :contentReference[oaicite:2]{index=2} [4] S. M. Ganie et al., \"Improved liver disease prediction using ensemble learning,\" BMC Medical Informatics, 2024, doi: 10.1186/s12911-024-02550-y. :contentReference[oaicite:3]{index=3} [5] Z. Li et al., \"ML-based liver disease staging using arterial quantification,\" Scientific Reports, 2025, doi: 10.1038/s41598-025-87427-4. :contentReference[oaicite:4]{index=4} [6] J. Deng et al., \"ML framework for fatty liver disease risk assessment,\" BMC Public Health, 2024, doi: 10.1186/s12889-024-19882-z. :contentReference[oaicite:5]{index=5} [7] N. S. Punn et al., \"Liver fibrosis classification using ultrasound ML techniques,\" Abdominal Radiology, 2024, doi: 10.1007/s00261-023-04081-y. :contentReference[oaicite:6]{index=6} [8] Z. Zhang, \"Diagnosis of liver diseases based on AI,\" Biotechnol Genet Eng Rev, 2024, doi: 10.1080/02648725.2023.2193057. :contentReference[oaicite:7]{index=7} [9] A. Hussain et al., \"Deep learning for liver tumor classification,\" Applied AI, 2022, doi: 10.1080/08839514.2022.2055395 [10] N. Delfan et al., \"AI-driven liver disease detection using cascade model,\" 2024. [11] A. Karna et al., \"Dimensionality reduction in liver disease detection,\" 2024 [12] N. Delfan et al., \"Non-invasive detection of fatty liver disease using AI,\" 2024. :contentReference[oaicite:8]{index=8} [13] O. Yasar et al., \"ML for detection of NASH using claims data,\" 2022. :contentReference[oaicite:9]{index=9} [14] J. M. Lopez et al., \"ECG-based liver disease diagnosis using ML,\" 2024. :contentReference[oaicite:10]{index=10} [15] H. Zamanian et al., \"AI techniques for fatty liver disease diagnosis,\" 2024. :contentReference[oaicite:11]{index=11} [16] T. Das et al., \"Generalization issues in ML healthcare models,\" 2023. [17] K. Iyer et al., \"Comparative analysis of ML models for liver disease,\" 2022. [18] S. Mishra et al., \"Ensemble vs single model performance in healthcare,\" 2023 [19] L. Chen et al., \"Multimodal data integration in medical AI,\" 2024 [20] P. Nair et al., \"Challenges and future of AI in liver disease diagnosis,\" 2023.

Copyright

Copyright © 2026 Vaibhav Kalyanrao Tawale, Madhubala Chaudhari , Priyanka Tawale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.