Application of IRSA-BP neural network in diagnosing diabetes

• Published in: PloS one Vol. 20; no. 6; p. e0324759
• Main Authors: Zhang, Wan-Hua, Zhang, Zi-Xun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.06.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Adult; Algorithms; Artificial intelligence; Back propagation networks; Biology and Life Sciences; Care and treatment; Computer and Information Sciences; Data analysis; Datasets; Datasets as Topic; Diabetes; Diabetes mellitus; Diabetes Mellitus - diagnosis; Diagnosis; Diagnosis, Computer-Assisted - methods; Diagnostic tests; Disease prevention; Exercise; Feature selection; Female; Health aspects; Health care expenditures; Health care industry; Health risks; Humans; Learning algorithms; Machine learning; Medical diagnosis; Medical personnel; Medical research; Medicine and Health Sciences; Multilayers; Neural networks; Neural Networks, Computer; Optimization techniques; Performance evaluation; Physical Sciences; Prediction Algorithms; Predictions; Predictive Value of Tests; Pregnancy; Prognosis; Quality of life; Reptiles; Research and Analysis Methods; Search algorithms; Support vector machines; Young Adult; China; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0324759

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.