A Heart Disease Prediction Model Applying Deep Learning and Combined Fuzzy-Mutual Information Based Feature Selection

Despite remarkable advancements in medical technology, cardiovascular disease persists as a significant contributor to global mortality. This research addresses the imperative need for timely disease identification through the proposition of a heart disease prediction model, utilizing a merged 1D Convolutional Neural Network (CNN). The main aim of the research is to mitigate the inherent drawbacks of single-layer designs, with a particular emphasis on enhancing hierarchical feature extraction, broadening the model’s receptive fields, and facilitating more efficient non-linear transformations. The dataset has been collected from UCI data repository. The study methodology includes a new feature selection strategy that combines Mutual Information and Fuzzy Logic approaches, offering a subtle viewpoint that is not well covered in the literature at the moment. After undergoing extensive training, the output of two 1D CNNs are merged to provide an impressive average accuracy rate of 85%. The integrated 1D CNN model and Explainable AI methodologies yield promising outcomes in heart disease prediction, affirming the model’s potential as a screening instrument for early identification and intervention. By providing a strong framework for proactive cardiovascular health care, the study makes a substantial contribution to the nexus between medical science and machine learning.