Zhang Wan-Hua, Zhang Zi-Xun
School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China.
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
PLoS One. 2025 Jun 25;20(6):e0324759. doi: 10.1371/journal.pone.0324759. eCollection 2025.
Within the healthcare sector, the application of machine learning is gaining prominence, notably enhancing the efficiency and precision of diagnostic procedures. This study focuses on this key area of diabetes prediction and aims to develop an innovative prediction method. Using the data set published by Kare, this paper constructs and compares various intelligent systems based on multilayer algorithms, and specifically introduces improved reptile search algorithm (IRSA) to optimize the weight and threshold initialization of traditional backpropagation (BP) neural networks. This improvement aims to improve the network performance and accuracy in diabetes detection. In the study, the IRSA-BP hybrid algorithm and many other machine learning algorithms were used for diabetes prediction, and the algorithm performance was comprehensively evaluated using multiple classification metrics. The experimental results showed that the IRSA-BP algorithm performed the best among all the evaluated algorithms, with an accuracy of up to 83.6%, showing its superior performance in diabetes prediction. Therefore, the IRSA-BP classifier has an important potential for application in the medical field. It can assist medical professionals to identify diabetes risk earlier and assess the condition more accurately, thus improving diagnostic efficiency and accuracy. This is important for early intervention and treatment of patients with diabetes and to improve their health status and quality of life.
在医疗保健领域，机器学习的应用日益突出，显著提高了诊断程序的效率和精度。本研究聚焦于糖尿病预测这一关键领域，旨在开发一种创新的预测方法。本文利用Kare发布的数据集，构建并比较了基于多层算法的各种智能系统，并特别引入改进的爬虫搜索算法（IRSA）来优化传统反向传播（BP）神经网络的权重和阈值初始化。这一改进旨在提高糖尿病检测中的网络性能和准确性。在该研究中，IRSA-BP混合算法和许多其他机器学习算法被用于糖尿病预测，并使用多种分类指标对算法性能进行了综合评估。实验结果表明，IRSA-BP算法在所有评估算法中表现最佳，准确率高达83.6%，显示出其在糖尿病预测方面的卓越性能。因此，IRSA-BP分类器在医疗领域具有重要的应用潜力。它可以帮助医疗专业人员更早地识别糖尿病风险，并更准确地评估病情，从而提高诊断效率和准确性。这对于糖尿病患者的早期干预和治疗以及改善他们的健康状况和生活质量至关重要。
