AI-based Clinical Decision Support for Heart Disease Prediction

Abstract

Cardiovascular disease is the leading cause of death worldwide, and early, data-driven risk identification can support timely clinical intervention. This paper presents a study of deep learning models for binary heart disease prediction on a large structured clinical dataset of 50,000 records described by 20 demographic, lifestyle, medical-history, and physiological attributes. After a preprocessing pipeline that recovers a mislabelled "no-alcohol" category, one-hot encodes categorical variables, and standardizes numerical features, we design and compare three deep neural architectures, a regularized deep neural network (DNN), a bidirectional long short-term memory network (BiLSTM), and a hybrid CNN–BiLSTM, against a logistic-regression baseline. All models are evaluated on a held-out test set using accuracy, precision, recall, F1-score, and the area under the ROC curve. The proposed DNN achieves the highest performance, with 99.68% accuracy, a 99.65% F1-score, and an ROC area of approximately 1.0, misclassifying only 24 of 7,500 test samples and outperforming the BiLSTM (98.95%), the CNN–BiLSTM (98.15%), and the linear baseline (92.11%). A feature-correlation and ablation analysis shows that the outcome is governed by a small set of non-linearly interacting risk factors, hypertension, age, total cholesterol, diabetes, and prior myocardial infarction, which explains why the deep models capture a decision boundary that the linear model cannot. Because the dataset is a synthetic benchmark, the results are reported transparently, and the need for external validation on real-world clinical cohorts is discussed.