A comparative analysis of machine learning algorithms with tree-structured parzen estimator for liver disease prediction

The liver is one of the most essential organs in the body, which helps with metabolism and keeping the body healthy. Successful treatments and better patient outcomes depend on early and correct Liver Disease (LD) diagnosis and identification. This study proposes a system for predicting the LD by combining the techniques of Machine Learning (ML) algorithms that include the Decision Tree, Random Forest, Extra Tree Classifier (ETC), LightGBM, and Adaboost, with the Tree-Structured Parzen Estimator (TPE) method for hyperparameter tuning. No previous literature research has utilized ML algorithms with TPE to predict LD. For this research, the Indian Liver Patients’ Dataset with 583 instances and 11 attributes was used. In the pre-processing of the data, techniques such as upsampling have been utilized to address the class imbalance problem. Normalization has been employed to scale the dataset, and feature selection has been applied to choose important features. The proposed model has been analyzed and compared using a 10-fold cross-validation process, with various evaluation metrics including accuracy, precision, recall, and F1-score. The model proposed in this study achieved the best level of accuracy while employing the ETC with the TPE approach, with a recorded accuracy of 95.8%. • Propose combining a Tree-Structured Parzen Estimator (TPE) method with machine learning algorithms. • Determine various hyperparameter values and settings for TPE. • Achieve the highest accuracy of 95.8% using TPE with an extra tree classifier. • Perform a comparative analysis of different evaluation metrics with and without the TPE method. • Show the proposed model could enhance cost-effectiveness and provide early predictive capabilities for physicians.