Development of a deep learning model for prediction of cardiovascular disease - Mohd Syafiq Asyraf, Suhaimi; Nor Azuana, Ramli; Lilik Jamilatul, Awalin (Penerbit UMP, 2025)
15.1% of medically certified deaths in 2023 were due to ischemic heart disease (IHD), according to Department of Statistics Malaysia (DOSM) statistics on causes of death in Malaysia published in October 2024. Despite the slight decline, IHD remains a significant health concern in Malaysia, especially among males and individuals aged 41–59 years, where it accounted for 19.8% of deaths in that age group. Regular checks are one approach to preventing heart disease in its early stages; however, they can be expensive and time-consuming. With the advancement of technology, people can now conveniently check their blood pressure, heart rate, and electrocardiogram (ECG) using smartwatches. However, since some people lead busy lives and occasionally forget to track or monitor their health through the applications, monitoring alone is insufficient. The primary goal of this research was to develop a deep learning model for predicting cardiovascular disease (CVD) using data from smartwatches, which offer non-invasive and real-time health monitoring capabilities. The research employs two deep learning techniques: Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. ECG and heart rate data were collected from 20 volunteers using local smartwatches supplemented with a publicly available dataset from Kaggle. Data pre-processing involved denoising ECG signals and normalising heart rate readings to ensure accuracy and reliability. The models were evaluated using precision, recall, F1-score, and accuracy metrics, achieving over 99 per cent across all measures. While both models demonstrated high predictive power, the LSTM model outperformed the CNN in computational efficiency, completing model training in 31 minutes compared to 87 minutes for the CNN. The study highlights the potential for wearable devices for real-time CVD monitoring and early diagnosis. Future work will explore the inclusion of additional data sources and advanced modelling involving ensemble techniques.