Hybrid CNN-LSTM Model for Enhanced Cardiac Disease Risk Prediction

Abstract

Cardiac Disease continues to be a foremost universal health issue every year, highlighting the need for early and accurate predictions, which are essential for preventing critical outcomes. Traditional clinical prediction models focus largely on structured clinical features such as Age, cholesterol, and blood pressure. This provides valuable insights for identifying patients at risk. However, structured characteristics largely fail to express the Unpredictable characteristics of their data. Existing approaches, such as predictive analytics models and other deep-learning architectures (like CNN +LSTM), have more limited capabilities because they either learn temporal patterns (LSTM) but lack strong feature extraction or extraction of spatial features, or do not model sequential relationships (CNN). A combined CNN+LSTM model for cardiac risk prediction on a clinical dataset is presented in this work. A well-established clinical dataset, such as the publicly available Clinical dataset, with 14 clinically validated features derived from real records, will be used as the model training point. Preprocessing will be performed on the dataset for the purpose of improving performance. Within this proposed architecture, the CNN layers will extract high-level feature patterns from clinical inputs, and the LSTM layers will learn structural dependencies that provide a relatively complete indicator of patient health to allow better decision-making and easier patient stratification. The experimental results indicated that the hybrid CNN+LSTM outperformed in prediction with 98.05% accuracy, 96.29% precision, and 95.56% recall, thereby supporting the conclusion that the integration of CNN and LSTM improves feature learning and better sequence learning improves Cardiac disease prediction.