A Systematic Literature Review of Probabilistic Model-Based Approaches for Heart Disease Prediction

Heart disease is a global health concern, contributing considerably to global death rates. Predicting heart disease accurately and on time is critical to improve healthcare outcomes. Recent technological breakthroughs have resulted in the creation of prediction models, which are increasingly being used in proactive healthcare efforts. Probabilistic techniques have gained relevance among these models due to their capacity to handle ambiguity in medical data. This paper undertakes a systematic literature review to evaluate the use of probabilistic model-based techniques in the prediction of heart disease. This paper presents the common research themes in heart disease prediction and probabilistic models, highlighting areas that need more attention. In addition, the paper discusses the effectiveness of probabilistic models in the context of heart disease prediction, drawing attention to their unique capacity to quantify prediction uncertainty. Overall, this paper highlights the promise that these probabilistic models have for improving diagnostic precision and efficacy in clinical settings dedicated to heart disease care.