DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction
Cardiovascular Disease (CVD) is one of the main causes of death in recent years. To overcome the challenges faced during diagnosing CVD at an early stage, deep learning has been used. With advancements in technology, the clinical practice in the health care industry is likely to transform significan...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/22/10516 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846154458257424384 |
|---|---|
| author | Aymin Javed Nadeem Javaid Nabil Alrajeh Muhammad Aslam |
| author_facet | Aymin Javed Nadeem Javaid Nabil Alrajeh Muhammad Aslam |
| author_sort | Aymin Javed |
| collection | DOAJ |
| description | Cardiovascular Disease (CVD) is one of the main causes of death in recent years. To overcome the challenges faced during diagnosing CVD at an early stage, deep learning has been used. With advancements in technology, the clinical practice in the health care industry is likely to transform significantly. To predict CVD, we constructed two models: Dense Belief Network (DB-Net) and Deep Vanilla Recurrent Network (DVR-Net). Proximity Weighted Random Affine Shadow sampling balancing technique is used for balancing the highly imbalanced Heart Disease Health Indicator dataset. SHapley Additive exPlanations exhibits each feature’s contribution. It is used to visualize features contribution to the output of DB-Net and DVR-Net in CVD prediction. Furthermore, 10-Fold Cross Validation is performed for evaluating the proposed models performance. Cross-dataset evaluation is also conducted on proposed models to see how well our proposed models generalize on unseen data. Various evaluation measures are used for assessment of models. The proposed DB-Net outperforms all the base models by achieving an accuracy of 91%, F1-score of 91%, precision of 93%, recall of 89%, and execution time of 1883 s on 30 epochs with batch size 32. The DVR-Net beats the state-of-art models with an accuracy of 90%, F1-score of 90%, precision of 90%, recall of 90%, and execution time of 2853 s on 30 epochs with batch size 32. |
| format | Article |
| id | doaj-art-55d9915860094d6d96e9743e4fea751e |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-55d9915860094d6d96e9743e4fea751e2024-11-26T17:49:04ZengMDPI AGApplied Sciences2076-34172024-11-0114221051610.3390/app142210516DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease PredictionAymin Javed0Nadeem Javaid1Nabil Alrajeh2Muhammad Aslam3ComSens Lab, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu 64002, TaiwanComSens Lab, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu 64002, TaiwanDepartment of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11633, Saudi ArabiaDepartment of Computer Science, Aberystwyth University, Aberystwyth SY23 3FL, UKCardiovascular Disease (CVD) is one of the main causes of death in recent years. To overcome the challenges faced during diagnosing CVD at an early stage, deep learning has been used. With advancements in technology, the clinical practice in the health care industry is likely to transform significantly. To predict CVD, we constructed two models: Dense Belief Network (DB-Net) and Deep Vanilla Recurrent Network (DVR-Net). Proximity Weighted Random Affine Shadow sampling balancing technique is used for balancing the highly imbalanced Heart Disease Health Indicator dataset. SHapley Additive exPlanations exhibits each feature’s contribution. It is used to visualize features contribution to the output of DB-Net and DVR-Net in CVD prediction. Furthermore, 10-Fold Cross Validation is performed for evaluating the proposed models performance. Cross-dataset evaluation is also conducted on proposed models to see how well our proposed models generalize on unseen data. Various evaluation measures are used for assessment of models. The proposed DB-Net outperforms all the base models by achieving an accuracy of 91%, F1-score of 91%, precision of 93%, recall of 89%, and execution time of 1883 s on 30 epochs with batch size 32. The DVR-Net beats the state-of-art models with an accuracy of 90%, F1-score of 90%, precision of 90%, recall of 90%, and execution time of 2853 s on 30 epochs with batch size 32.https://www.mdpi.com/2076-3417/14/22/10516Cardiovascular DiseaseDeep LearningHeart Failure10-Fold Cross ValidationSHapley Additive exPlanations |
| spellingShingle | Aymin Javed Nadeem Javaid Nabil Alrajeh Muhammad Aslam DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction Applied Sciences Cardiovascular Disease Deep Learning Heart Failure 10-Fold Cross Validation SHapley Additive exPlanations |
| title | DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction |
| title_full | DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction |
| title_fullStr | DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction |
| title_full_unstemmed | DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction |
| title_short | DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction |
| title_sort | db net and dvr net optimized new deep learning models for efficient cardiovascular disease prediction |
| topic | Cardiovascular Disease Deep Learning Heart Failure 10-Fold Cross Validation SHapley Additive exPlanations |
| url | https://www.mdpi.com/2076-3417/14/22/10516 |
| work_keys_str_mv | AT ayminjaved dbnetanddvrnetoptimizednewdeeplearningmodelsforefficientcardiovasculardiseaseprediction AT nadeemjavaid dbnetanddvrnetoptimizednewdeeplearningmodelsforefficientcardiovasculardiseaseprediction AT nabilalrajeh dbnetanddvrnetoptimizednewdeeplearningmodelsforefficientcardiovasculardiseaseprediction AT muhammadaslam dbnetanddvrnetoptimizednewdeeplearningmodelsforefficientcardiovasculardiseaseprediction |