Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction
Abstract Heart failure is a leading cause of premature death, especially among individuals with a sedentary lifestyle. Early and accurate detection is essential to prevent the progression of this situation. However, many existing prediction systems failed to detect early and accurately, also taking...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88277-w |
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| author | G. Kajeeth Kumar S. Muthurajkumar |
| author_facet | G. Kajeeth Kumar S. Muthurajkumar |
| author_sort | G. Kajeeth Kumar |
| collection | DOAJ |
| description | Abstract Heart failure is a leading cause of premature death, especially among individuals with a sedentary lifestyle. Early and accurate detection is essential to prevent the progression of this situation. However, many existing prediction systems failed to detect early and accurately, also taking more time to detect. To address these issues, we propose an advanced heart failure detection model that combines one-dimensional chaotic maps and a Gradient-Based Optimizer (GBO) called Chaotic Gradient-Based Optimizer (CGBO). This approach improves feature selection by effectively selecting the most crucial features related to the risk of heart failure. Additionally, we introduce the Fuzzy Temporal Optimized Convolutional Neural Network (FTOCNN) classifier that incorporates CGBO and fuzzy temporal rules to enhance detection accuracy. The proposed model is evaluated using the UCI heart dataset and Electronic Health Records (EHRs) and its performance is assessed through statistical measures, classification metrics, and a Wilcoxon rank-sum p-test. Furthermore, a tenfold cross-validation process ensures a comprehensive evaluation and the proposed method outperforms different Machine Learning (ML) / Deep Learning (DL) classifiers. The experimental findings reveal that CGBO significantly improves the predictive performance of the FTOCNN classifier by achieving 94% accuracy in EHR and enhances the reliability of heart failure detection compared to existing systems. |
| format | Article |
| id | doaj-art-abf78cd622d5476caa53b6d7bd7662a5 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-abf78cd622d5476caa53b6d7bd7662a52025-02-02T12:16:18ZengNature PortfolioScientific Reports2045-23222025-01-0115112710.1038/s41598-025-88277-wChaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure predictionG. Kajeeth Kumar0S. Muthurajkumar1Department of Computer Technology, MIT Campus, Anna UniversityDepartment of Computer Technology, MIT Campus, Anna UniversityAbstract Heart failure is a leading cause of premature death, especially among individuals with a sedentary lifestyle. Early and accurate detection is essential to prevent the progression of this situation. However, many existing prediction systems failed to detect early and accurately, also taking more time to detect. To address these issues, we propose an advanced heart failure detection model that combines one-dimensional chaotic maps and a Gradient-Based Optimizer (GBO) called Chaotic Gradient-Based Optimizer (CGBO). This approach improves feature selection by effectively selecting the most crucial features related to the risk of heart failure. Additionally, we introduce the Fuzzy Temporal Optimized Convolutional Neural Network (FTOCNN) classifier that incorporates CGBO and fuzzy temporal rules to enhance detection accuracy. The proposed model is evaluated using the UCI heart dataset and Electronic Health Records (EHRs) and its performance is assessed through statistical measures, classification metrics, and a Wilcoxon rank-sum p-test. Furthermore, a tenfold cross-validation process ensures a comprehensive evaluation and the proposed method outperforms different Machine Learning (ML) / Deep Learning (DL) classifiers. The experimental findings reveal that CGBO significantly improves the predictive performance of the FTOCNN classifier by achieving 94% accuracy in EHR and enhances the reliability of heart failure detection compared to existing systems.https://doi.org/10.1038/s41598-025-88277-wFeature selectionFuzzy temporal rulesOptimized CNNCGBOChaotic mapDisease prediction |
| spellingShingle | G. Kajeeth Kumar S. Muthurajkumar Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction Scientific Reports Feature selection Fuzzy temporal rules Optimized CNN CGBO Chaotic map Disease prediction |
| title | Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction |
| title_full | Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction |
| title_fullStr | Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction |
| title_full_unstemmed | Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction |
| title_short | Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction |
| title_sort | chaotic gradient based optimization with fuzzy temporal optimized cnn for heart failure prediction |
| topic | Feature selection Fuzzy temporal rules Optimized CNN CGBO Chaotic map Disease prediction |
| url | https://doi.org/10.1038/s41598-025-88277-w |
| work_keys_str_mv | AT gkajeethkumar chaoticgradientbasedoptimizationwithfuzzytemporaloptimizedcnnforheartfailureprediction AT smuthurajkumar chaoticgradientbasedoptimizationwithfuzzytemporaloptimizedcnnforheartfailureprediction |