Development of a deep learning model for prediction of cardiovascular disease
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, especiall...
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2025
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| Online Access: | https://umpir.ump.edu.my/id/eprint/45502/ |
| _version_ | 1848827435482611712 |
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| author | Mohd Syafiq Asyraf, Suhaimi Nor Azuana, Ramli Lilik Jamilatul, Awalin |
| author_facet | Mohd Syafiq Asyraf, Suhaimi Nor Azuana, Ramli Lilik Jamilatul, Awalin |
| author_sort | Mohd Syafiq Asyraf, Suhaimi |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T04:00:40Z |
| format | Article |
| id | ump-45502 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T04:00:40Z |
| publishDate | 2025 |
| publisher | Penerbit UMP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-455022025-08-28T01:15:49Z https://umpir.ump.edu.my/id/eprint/45502/ Development of a deep learning model for prediction of cardiovascular disease Mohd Syafiq Asyraf, Suhaimi Nor Azuana, Ramli Lilik Jamilatul, Awalin QA75 Electronic computers. Computer science 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. Penerbit UMP 2025-03-31 Article PeerReviewed pdf en cc_by_nc_4 https://umpir.ump.edu.my/id/eprint/45502/1/12496.pdf Mohd Syafiq Asyraf, Suhaimi and Nor Azuana, Ramli and Lilik Jamilatul, Awalin (2025) Development of a deep learning model for prediction of cardiovascular disease. Data Analytics and Applied Mathematics (DAAM), 6 (1). pp. 53-64. ISSN 2773-4854. (Published) https://journal.ump.edu.my/daam/article/view/12496 |
| spellingShingle | QA75 Electronic computers. Computer science Mohd Syafiq Asyraf, Suhaimi Nor Azuana, Ramli Lilik Jamilatul, Awalin Development of a deep learning model for prediction of cardiovascular disease |
| title | Development of a deep learning model for prediction of cardiovascular disease |
| title_full | Development of a deep learning model for prediction of cardiovascular disease |
| title_fullStr | Development of a deep learning model for prediction of cardiovascular disease |
| title_full_unstemmed | Development of a deep learning model for prediction of cardiovascular disease |
| title_short | Development of a deep learning model for prediction of cardiovascular disease |
| title_sort | development of a deep learning model for prediction of cardiovascular disease |
| topic | QA75 Electronic computers. Computer science |
| url | https://umpir.ump.edu.my/id/eprint/45502/ https://umpir.ump.edu.my/id/eprint/45502/ |