Machine learning (ML) has emerged as a vital resource in the healthcare industry for enhancing disease classification and predictive analytics. This study focuses on liver diseases, investigating how ML models can efficiently predict and classify these conditions based on clinical data. A robust dataset, which includes a variety of patient details and medical parameters, forms the basis of the analysis. The study employs diverse ML approaches that are rigorously trained and tested to detect intricate data patterns and correlations, aiming to refine diagnostic accuracy and prognostic insights. Evaluation metrics such as F1-score, recall, precision, and accuracy are used to measure the reliability and effectiveness of the algorithms. By leveraging ML, this work strives to improve the early identification and classification of liver diseases and to provide healthcare providers with advanced tools for individualized treatment planning and better patient outcomes.
S et al. (Thu,) studied this question.