A Multitask Deep Learning Model for Predicting Myocardial Infarction Complications
Myocardial infarction is one of the most severe forms of ischemic heart disease, associated with high mortality and disability worldwide. The accurate and reliable prediction of adverse cardiovascular events is critical for developing effective treatment strategies and improving outcomes in cardiac...
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MDPI AG
2025-05-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/5/520 |
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| author | Fazliddin Makhmudov Normakhmad Ravshanov Dilshot Akhmedov Oleg Pekos Dilmurod Turimov Young-Im Cho |
| author_facet | Fazliddin Makhmudov Normakhmad Ravshanov Dilshot Akhmedov Oleg Pekos Dilmurod Turimov Young-Im Cho |
| author_sort | Fazliddin Makhmudov |
| collection | DOAJ |
| description | Myocardial infarction is one of the most severe forms of ischemic heart disease, associated with high mortality and disability worldwide. The accurate and reliable prediction of adverse cardiovascular events is critical for developing effective treatment strategies and improving outcomes in cardiac rehabilitation. Traditional prognostic models, such as the GRACE and TIMI scores, often lack the flexibility to incorporate a wide range of contemporary clinical predictors. Therefore, machine learning methods, particularly deep neural networks, have recently emerged as promising alternatives capable of enhancing predictive accuracy and enabling more personalized care. This study presents a multitask deep learning model designed to simultaneously address two related tasks: multidimensional binary classification of myocardial infarction complications and multiclass classification of mortality causes. The model was trained on a dataset of 1700 patients, encompassing 111 clinical and demographic features. Experimental results demonstrate high predictive accuracy and the model’s capacity to capture complex interactions among risk factors, suggesting its potential as a valuable tool for clinical decision support in cardiology. Comparative analysis confirms that the proposed multitask approach performs comparably to, or better than, conventional machine learning models. Future research will focus on refining the model and validating its generalizability in real-world clinical environments. |
| format | Article |
| id | doaj-art-bd91692fb524482aa06c100680bc10fa |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-bd91692fb524482aa06c100680bc10fa2025-08-20T01:56:25ZengMDPI AGBioengineering2306-53542025-05-0112552010.3390/bioengineering12050520A Multitask Deep Learning Model for Predicting Myocardial Infarction ComplicationsFazliddin Makhmudov0Normakhmad Ravshanov1Dilshot Akhmedov2Oleg Pekos3Dilmurod Turimov4Young-Im Cho5Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of KoreaDigital Technologies and Artificial Intelligence Development Research Institute, 17A, Buz-2, Tashkent 100125, UzbekistanDigital Technologies and Artificial Intelligence Development Research Institute, 17A, Buz-2, Tashkent 100125, UzbekistanDigital Technologies and Artificial Intelligence Development Research Institute, 17A, Buz-2, Tashkent 100125, UzbekistanDepartment of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of KoreaDepartment of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of KoreaMyocardial infarction is one of the most severe forms of ischemic heart disease, associated with high mortality and disability worldwide. The accurate and reliable prediction of adverse cardiovascular events is critical for developing effective treatment strategies and improving outcomes in cardiac rehabilitation. Traditional prognostic models, such as the GRACE and TIMI scores, often lack the flexibility to incorporate a wide range of contemporary clinical predictors. Therefore, machine learning methods, particularly deep neural networks, have recently emerged as promising alternatives capable of enhancing predictive accuracy and enabling more personalized care. This study presents a multitask deep learning model designed to simultaneously address two related tasks: multidimensional binary classification of myocardial infarction complications and multiclass classification of mortality causes. The model was trained on a dataset of 1700 patients, encompassing 111 clinical and demographic features. Experimental results demonstrate high predictive accuracy and the model’s capacity to capture complex interactions among risk factors, suggesting its potential as a valuable tool for clinical decision support in cardiology. Comparative analysis confirms that the proposed multitask approach performs comparably to, or better than, conventional machine learning models. Future research will focus on refining the model and validating its generalizability in real-world clinical environments.https://www.mdpi.com/2306-5354/12/5/520cardiac rehabilitationmultitask neuron networkloss functionclass imbalanceclinical factors |
| spellingShingle | Fazliddin Makhmudov Normakhmad Ravshanov Dilshot Akhmedov Oleg Pekos Dilmurod Turimov Young-Im Cho A Multitask Deep Learning Model for Predicting Myocardial Infarction Complications Bioengineering cardiac rehabilitation multitask neuron network loss function class imbalance clinical factors |
| title | A Multitask Deep Learning Model for Predicting Myocardial Infarction Complications |
| title_full | A Multitask Deep Learning Model for Predicting Myocardial Infarction Complications |
| title_fullStr | A Multitask Deep Learning Model for Predicting Myocardial Infarction Complications |
| title_full_unstemmed | A Multitask Deep Learning Model for Predicting Myocardial Infarction Complications |
| title_short | A Multitask Deep Learning Model for Predicting Myocardial Infarction Complications |
| title_sort | multitask deep learning model for predicting myocardial infarction complications |
| topic | cardiac rehabilitation multitask neuron network loss function class imbalance clinical factors |
| url | https://www.mdpi.com/2306-5354/12/5/520 |
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