An efficient artificial neural network-based optimization techniques for the early prediction of coronary heart disease: comprehensive analysis
Abstract Coronary heart disease (CHD) is the world’s leading cause of death, contributing to a high mortality rate. This emphasizes the requirement for an advanced decision support system in order to evaluate the risk of CHD. This study presents an Artificial Neural Network (ANN) based intelligent h...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85765-x |
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| author | Subhash Mondal Ranjan Maity Amitava Nag |
| author_facet | Subhash Mondal Ranjan Maity Amitava Nag |
| author_sort | Subhash Mondal |
| collection | DOAJ |
| description | Abstract Coronary heart disease (CHD) is the world’s leading cause of death, contributing to a high mortality rate. This emphasizes the requirement for an advanced decision support system in order to evaluate the risk of CHD. This study presents an Artificial Neural Network (ANN) based intelligent healthcare system to predict the risk of CHD. The proposed ANN model is trained using the Framingham Heart Study (FHS) dataset, which comprises 4240 data instances and 15 potential risk factors. To combat overfitting, the proposed model uses four hidden dense layers with dropout rates ranging from 0.5 to 0.2. Also, two activation functions, ReLU and LeakyReLU, are used in conjunction with four optimizers: Adam, SGD, RMSProp, and AdaDelta to fine-tune the parameters and minimize the loss functions. Moreover, three sophisticated preprocessing methods, SMOTE, SMOTETomek, and SMOTEENN, along with the proposed two-stage sampling approach, are applied to address the target class data imbalance. Experimental results demonstrate that the Adam optimizer coupled with the ReLU activation function and the combined sequential effect of SMOTEENN and SMOTETomek’s two-stage sampling approach achieved superior performance. The validation accuracy reached 96.25% with a recall value of 0.98, outperforming existing approaches reported in the literature. The combined effect of approaches will be evidence of the modern healthcare decision-making support system for the risk prediction of CHD. |
| format | Article |
| id | doaj-art-6351ebed44ee4349861e4435ae03c952 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-6351ebed44ee4349861e4435ae03c9522025-02-09T12:34:28ZengNature PortfolioScientific Reports2045-23222025-02-0115112410.1038/s41598-025-85765-xAn efficient artificial neural network-based optimization techniques for the early prediction of coronary heart disease: comprehensive analysisSubhash Mondal0Ranjan Maity1Amitava Nag2Computer Science and Engineering, Central Institute of Technology KokrajharComputer Science and Engineering, Central Institute of Technology KokrajharComputer Science and Engineering, Central Institute of Technology KokrajharAbstract Coronary heart disease (CHD) is the world’s leading cause of death, contributing to a high mortality rate. This emphasizes the requirement for an advanced decision support system in order to evaluate the risk of CHD. This study presents an Artificial Neural Network (ANN) based intelligent healthcare system to predict the risk of CHD. The proposed ANN model is trained using the Framingham Heart Study (FHS) dataset, which comprises 4240 data instances and 15 potential risk factors. To combat overfitting, the proposed model uses four hidden dense layers with dropout rates ranging from 0.5 to 0.2. Also, two activation functions, ReLU and LeakyReLU, are used in conjunction with four optimizers: Adam, SGD, RMSProp, and AdaDelta to fine-tune the parameters and minimize the loss functions. Moreover, three sophisticated preprocessing methods, SMOTE, SMOTETomek, and SMOTEENN, along with the proposed two-stage sampling approach, are applied to address the target class data imbalance. Experimental results demonstrate that the Adam optimizer coupled with the ReLU activation function and the combined sequential effect of SMOTEENN and SMOTETomek’s two-stage sampling approach achieved superior performance. The validation accuracy reached 96.25% with a recall value of 0.98, outperforming existing approaches reported in the literature. The combined effect of approaches will be evidence of the modern healthcare decision-making support system for the risk prediction of CHD.https://doi.org/10.1038/s41598-025-85765-xCoronary heart diseaseArtificial neural networksOptimization techniquesAdam optimizerPerformance efficiencyActivation functions |
| spellingShingle | Subhash Mondal Ranjan Maity Amitava Nag An efficient artificial neural network-based optimization techniques for the early prediction of coronary heart disease: comprehensive analysis Scientific Reports Coronary heart disease Artificial neural networks Optimization techniques Adam optimizer Performance efficiency Activation functions |
| title | An efficient artificial neural network-based optimization techniques for the early prediction of coronary heart disease: comprehensive analysis |
| title_full | An efficient artificial neural network-based optimization techniques for the early prediction of coronary heart disease: comprehensive analysis |
| title_fullStr | An efficient artificial neural network-based optimization techniques for the early prediction of coronary heart disease: comprehensive analysis |
| title_full_unstemmed | An efficient artificial neural network-based optimization techniques for the early prediction of coronary heart disease: comprehensive analysis |
| title_short | An efficient artificial neural network-based optimization techniques for the early prediction of coronary heart disease: comprehensive analysis |
| title_sort | efficient artificial neural network based optimization techniques for the early prediction of coronary heart disease comprehensive analysis |
| topic | Coronary heart disease Artificial neural networks Optimization techniques Adam optimizer Performance efficiency Activation functions |
| url | https://doi.org/10.1038/s41598-025-85765-x |
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