The first people's Hospital of Baiyin, Baiyin, Gansu 730900, China.
The second people's Hospital of Baiyin, Baiyin, Gansu 730900, China.
Int J Cardiol. 2024 Dec 1;416:132506. doi: 10.1016/j.ijcard.2024.132506. Epub 2024 Aug 30.
Early diagnosis of heart disease is crucial, as it's one of the leading causes of death globally. Machine learning algorithms can be a powerful tool in achieving this goal. Therefore, this article aims to increase the accuracy of predicting heart disease using machine learning algorithms. Five classification models are explored: eXtreme Gradient Boosting (XGBC), Random Forest Classifier (RFC), Decision Tree Classifier (DTC), K-Nearest Neighbors Classifier (KNNC), and Logistic Regression Classifier (LRC). Additionally, four optimizers are evaluated: Slime mold Optimization Algorithm, Forest Optimization Algorithm, Pathfinder algorithm, and Giant Armadillo Optimization. To ensure robust model selection, a feature selection technique utilizing k-fold cross-validation is employed. This method identifies the most relevant features from the data, potentially improving model performance. The top three performing models are then coupled with the optimization algorithms to potentially enhance their generalizability and accuracy in predicting heart failure. In the final stage, the shortlisted models (XGBC, RFC, and DTC) were assessed using performance metrics like accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). This rigorous evaluation identified the XGGA hybrid model as the top performer, demonstrating its effectiveness in predicting heart failure. XGGA achieved impressive metrics, with an accuracy, precision, recall, and F1-score of 0.972 in the training phase, underscoring its robustness. Notably, the model's predictions deviated by less than 5.5 % for patients classified as alive and by less than 1.2 % for those classified as deceased compared to the actual outcomes, reflecting minimal error and high predictive reliability. In contrast, the DTC base model was the least effective, with an accuracy of 0.840 and a precision of 0.847. Overall, the optimization using the GAO algorithm significantly enhanced the performance of the models, highlighting the benefits of this approach.
早期诊断心脏病至关重要，因为它是全球主要死亡原因之一。机器学习算法可以成为实现这一目标的有力工具。因此，本文旨在通过机器学习算法提高预测心脏病的准确性。本文探索了五种分类模型：极端梯度提升（XGBC）、随机森林分类器（RFC）、决策树分类器（DTC）、K-最近邻分类器（KNNC）和逻辑回归分类器（LRC）。此外，还评估了四种优化器：粘菌优化算法、森林优化算法、寻路算法和巨型犰狳优化算法。为了确保稳健的模型选择，采用了利用 k 折交叉验证的特征选择技术。这种方法从数据中识别出最相关的特征，可能会提高模型的性能。然后，将排名前三的模型与优化算法相结合，以提高其在预测心力衰竭方面的泛化能力和准确性。在最后阶段，使用性能指标（如准确性、精度、召回率、F1 分数和马氏相关系数（MCC））评估了入围模型（XGBC、RFC 和 DTC）。这种严格的评估确定了 XGGA 混合模型是表现最佳的模型，证明了其在预测心力衰竭方面的有效性。XGGA 在训练阶段的准确率、精度、召回率和 F1 分数分别达到 0.972，这表明其稳健性。值得注意的是，与实际结果相比，模型对被分类为存活的患者的预测偏差小于 5.5%，对被分类为死亡的患者的预测偏差小于 1.2%，这反映了极小的误差和高预测可靠性。相比之下，DTC 基础模型的效果最差，其准确率为 0.840，精度为 0.847。总的来说，使用 GAO 算法进行优化显著提高了模型的性能，突出了这种方法的优势。
