Kumar G Kajeeth, Muthurajkumar S
Department of Computer Technology, MIT Campus, Anna University, Chennai, Tamil Nadu, 600044, India.
Sci Rep. 2025 Jan 31;15(1):3867. doi: 10.1038/s41598-025-88277-w.
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.
心力衰竭是过早死亡的主要原因，尤其是在久坐不动的人群中。早期准确检测对于防止这种情况的发展至关重要。然而，许多现有的预测系统未能早期准确检测，而且检测时间更长。为了解决这些问题，我们提出了一种先进的心力衰竭检测模型，该模型结合了一维混沌映射和一种基于梯度的优化器（GBO），称为基于混沌梯度的优化器（CGBO）。这种方法通过有效选择与心力衰竭风险相关的最关键特征来改进特征选择。此外，我们引入了模糊时间优化卷积神经网络（FTOCNN）分类器，该分类器结合了CGBO和模糊时间规则以提高检测准确性。使用UCI心脏数据集和电子健康记录（EHR）对所提出的模型进行评估，并通过统计量度、分类指标和威尔科克森秩和p检验来评估其性能。此外，十折交叉验证过程确保了全面评估，并且所提出的方法优于不同的机器学习（ML）/深度学习（DL）分类器。实验结果表明，CGBO通过在EHR中实现94%的准确率显著提高了FTOCNN分类器的预测性能，并且与现有系统相比提高了心力衰竭检测的可靠性。
