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R. Jeevitha is an Assistant Professor in the Department of Computer Science and Engineering at Narasimha Reddy Engineering College, Maisammaguda, Telangana, India. Her academic role involves teaching and research in computer science and engineering. Her professional activities contribute to computing education. Her contribution focuses on AI-based liver disease prediction and patient risk assessment.
Liver ailments constitute a significant worldwide health issue owing to their correlation with metabolic disorders, viral infections, alcohol intake, drug-induced toxicity, and various chronic health issues. This chapter examines the prediction of liver disease and patient risk assessment with AI, emphasising data preprocessing, feature selection, predictive modelling, risk stratification, model interpretability, and clinical decision support. The discourse encompasses machine learning algorithms including logistic regression, decision trees, random forests, support vector machines, gradient boosting, and artificial neural networks. A theoretical AI-driven framework is suggested to amalgamate patient information, assess liver disease risk, categorise risk tiers, and deliver comprehensible forecasts for healthcare practitioners. The section additionally explores issues related to data integrity, class disparity, confidentiality, equity, clinical verification, and implementation. The suggested methodology is to facilitate preliminary screening and evidence-based clinical evaluation, highlighting that AI forecasts need to augment, not supplant, expert medical discernment.