Early Heart Attack Detection Using Hybrid Deep Learning Techniques
Given the significant risk that heart disease, particularly heart attacks, poses to individuals’ lives, it is crucial to develop effective techniques for early detection. Advanced machine learning and deep learning algorithms have the ability to predict heart attacks by analyzing a patient’s medical...
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MDPI AG
2025-04-01
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| author | Niga Amanj Hussain Aree Ali Mohammed |
| author_facet | Niga Amanj Hussain Aree Ali Mohammed |
| author_sort | Niga Amanj Hussain |
| collection | DOAJ |
| description | Given the significant risk that heart disease, particularly heart attacks, poses to individuals’ lives, it is crucial to develop effective techniques for early detection. Advanced machine learning and deep learning algorithms have the ability to predict heart attacks by analyzing a patient’s medical history and overall health. These algorithms can process large datasets, extracting valuable insights that help mitigate the risk of fatal outcomes. This study integrates a deep learning approach to predict and detect heart attacks early by classifying patient data as normal or abnormal. The proposed model combines a Convolutional Neural Network (CNN) with self-attention, leveraging the self-attention mechanism to focus on the most critical aspects of the sequence. Since heart attack risk is closely tied to the changes in vital signs over time, this approach enables the model to learn and assign appropriate weights to each input component. Improvements and modifications to the hybrid model resulted in a 98.71% accuracy rate during testing. The model’s strong performance on evaluation metrics shows its potential effectiveness in detecting heart attacks. |
| format | Article |
| id | doaj-art-7fde5baa89d1408eb776269d9c06f144 |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-7fde5baa89d1408eb776269d9c06f1442025-08-20T03:14:39ZengMDPI AGInformation2078-24892025-04-0116533410.3390/info16050334Early Heart Attack Detection Using Hybrid Deep Learning TechniquesNiga Amanj Hussain0Aree Ali Mohammed1Computer Science Department, College of Science, University of Sulaimani, Sulaymaniyah 46001, IraqComputer Science Department, College of Science, University of Sulaimani, Sulaymaniyah 46001, IraqGiven the significant risk that heart disease, particularly heart attacks, poses to individuals’ lives, it is crucial to develop effective techniques for early detection. Advanced machine learning and deep learning algorithms have the ability to predict heart attacks by analyzing a patient’s medical history and overall health. These algorithms can process large datasets, extracting valuable insights that help mitigate the risk of fatal outcomes. This study integrates a deep learning approach to predict and detect heart attacks early by classifying patient data as normal or abnormal. The proposed model combines a Convolutional Neural Network (CNN) with self-attention, leveraging the self-attention mechanism to focus on the most critical aspects of the sequence. Since heart attack risk is closely tied to the changes in vital signs over time, this approach enables the model to learn and assign appropriate weights to each input component. Improvements and modifications to the hybrid model resulted in a 98.71% accuracy rate during testing. The model’s strong performance on evaluation metrics shows its potential effectiveness in detecting heart attacks.https://www.mdpi.com/2078-2489/16/5/334heart attack predictionhybrid CNN–self attention modeldeep learning in healthcaretime series dataabnormality detection |
| spellingShingle | Niga Amanj Hussain Aree Ali Mohammed Early Heart Attack Detection Using Hybrid Deep Learning Techniques Information heart attack prediction hybrid CNN–self attention model deep learning in healthcare time series data abnormality detection |
| title | Early Heart Attack Detection Using Hybrid Deep Learning Techniques |
| title_full | Early Heart Attack Detection Using Hybrid Deep Learning Techniques |
| title_fullStr | Early Heart Attack Detection Using Hybrid Deep Learning Techniques |
| title_full_unstemmed | Early Heart Attack Detection Using Hybrid Deep Learning Techniques |
| title_short | Early Heart Attack Detection Using Hybrid Deep Learning Techniques |
| title_sort | early heart attack detection using hybrid deep learning techniques |
| topic | heart attack prediction hybrid CNN–self attention model deep learning in healthcare time series data abnormality detection |
| url | https://www.mdpi.com/2078-2489/16/5/334 |
| work_keys_str_mv | AT nigaamanjhussain earlyheartattackdetectionusinghybriddeeplearningtechniques AT areealimohammed earlyheartattackdetectionusinghybriddeeplearningtechniques |