Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt.
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Sci Rep. 2024 Oct 8;14(1):23386. doi: 10.1038/s41598-024-72792-3.
The classification of chronic diseases has been a prominent research focus in public health, extensively leveraging machine learning algorithms. One of these chronic diseases that has significant rates of occurrence all around the world is diabetes, which is a disease by itself. Many academics are working to construct robust machine-learning algorithms for accurate categorization, given the prevalence of this chronic disease. A revolutionary methodology that can accurately categorize diabetic disease is the focus of this study, which aims to provide new methods. The proposed technique in this work is based on developing a novel feature selection method, DWWPA, which stands for dynamic waterwheel plant algorithm. The DWWPA algorithm is utilized in the process of optimizing the K-nearest neighbors (KNN) model in order to improve the accuracy of its classification. In the feature selection process, a binary representation of this method is called binary DWWPA (bDWWPA). Several different machine learning models and optimization techniques are compared to the strategy that has been presented. When categorizing diabetes cases in the dataset, the findings demonstrate the superiority and success of the proposed method. Furthermore, several different statistical analysis techniques, such as Analyses of variance (ANOVA) and Wilcoxon signed-rank test, are carried out to investigate the statistical difference and importance of the suggested strategy in contrast to the other ways at the same level of competition. The conclusions of these tests were consistent with what was anticipated they would be. Based on the suggested feature selection and the optimization of the KNN model, the proposed method has an accuracy of 98.9% when taken as an entire. The suggested method was useful in accurately classifying diabetic disease, as evidenced by the fact that it achieved a higher level of accuracy than the contemporary approaches.
慢性病分类一直是公共卫生领域的一个重要研究焦点，广泛应用机器学习算法。其中一种在全球范围内发病率很高的慢性病是糖尿病，它本身就是一种疾病。鉴于这种慢性病的普遍存在，许多学者正在努力构建强大的机器学习算法以进行准确分类。本研究的重点是一种可以准确分类糖尿病的革命性方法，旨在提供新的方法。这项工作中提出的技术基于开发一种新的特征选择方法，即动态水轮植物算法（DWWPA）。DWWPA 算法用于优化 K-最近邻（KNN）模型，以提高其分类的准确性。在特征选择过程中，这种方法的二进制表示称为二进制 DWWPA（bDWWPA）。将几种不同的机器学习模型和优化技术与所提出的策略进行比较。在对数据集进行糖尿病病例分类时，研究结果表明了所提出方法的优越性和成功性。此外，还进行了几种不同的统计分析技术，如方差分析（ANOVA）和 Wilcoxon 符号秩检验，以研究所提出策略与其他竞争方法相比的统计差异和重要性。这些测试的结论与预期的结果一致。基于所提出的特征选择和 KNN 模型的优化，所提出的方法的整体准确率为 98.9%。所提出的方法在准确分类糖尿病方面非常有效，因为它的准确率高于当代方法。
