Liver Patient Analysis Using Machine Learning Techniques

Liver diseases are among the leading causes of morbidity and mortality worldwide, often diagnosed in advanced stages due to the absence of early symptoms. Early detection plays a crucial role in reducing complications and improving patient outcomes. This study aims to develop a machine learning-based predictive model for liver disease diagnosis using structured clinical datasets. Various algorithms, including Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), were evaluated for their effectiveness in classifying patients based on their medical history and test results. The dataset includes essential biomarkers such as bilirubin levels, enzyme counts, and protein ratios, which serve as primary indicators of liver health. After rigorous model training and evaluation, Random Forest emerged as the most accurate classifier, achieving an accuracy of 89%, making it a viable tool for assisting healthcare professionals in early diagnosis. The findings of this study suggest that AI-driven predictive analytics can significantly enhance medical decision-making, reducing dependency on expensive diagnostic procedures and providing timely interventions for at- risk patients.