Generative AI System for Explainable Diagnosis and Risk Prediction

Heart diseases are still one of the most significant global health problems leading to a considerable number of deaths globally. Detection at an early stage and accurate prediction of cardiovascular problems can help in saving the lives that are lost by these diseases through preventive measures and timely treatment. Usually, diagnostic techniques involve the manual interpretation of clinical parameters where the results can be subjective and human errors can be made. In the last few years, machine learning (ML) has been a revolutionary tool in healthcare analytics by providing intelligent, data-driven decision support systems that can understand complex and nonlinear relationships in medical datasets. This research paper presents a comparative study of various ML algorithms such as Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting to forecast heart disease from publicly available datasets like the UCI Cleveland Heart Disease dataset. This paper mainly focuses on data preprocessing, feature selection, and hyper parameter tuning to gain the trustworthiness of the model, which can then be used to apply the algorithm towards medical prediction. Specific metrics to assess and evaluate the performance of the models include accuracy, precision, recall, F1-score, and ROC-AUC. Experiments results show that ensemble-based models perform better than traditional classifiers and, thus, can achieve a prediction accuracy of more than 92% of the time. The outcomes indicate the capacity of ML algorithms to be used as a supportive tool by clinicians in risk assessment and prognosis of heart-related issues. Next, the work will be directed towards combining deep learning with real-time patient monitoring for ongoing health assessment.