Enhancing Heart Disease Detection Using Multilayer Perceptron, Bidirectional LSTM, Support Vector Machine, and Random Forest on a Cardiovascular Disease Dataset

Heart disease remains a leading cause of mortality worldwide, highlighting the need for accurate and early diagnosis. This study explores the application of multiple machine learning and deep learning models—Multilayer Perceptron (MLP), Bidirectional Long Short-Term Memory (BiLSTM), Support Vector Machine (SVM), and Random Forest (RF)—to enhance the predictive performance of heart disease detection using the publicly available Cardiovascular Disease dataset. The dataset undergoes preprocessing, normalization, and model-specific preparation before being used to train and test each algorithm. The performance of these models is evaluated using standard classification metrics: Accuracy, Precision, Recall (Sensitivity), Specificity, and F1 Score. Experimental results demonstrate that deep learning models like BiLSTM can capture complex patterns in sequential data, while classical machine learning models such as RF and SVM offer strong baseline performance. This comparative analysis provides valuable insights into model selection for medical diagnostics and lays the groundwork for future integration into decision-support systems.