{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:05Z","timestamp":1788912005700,"version":"build-2803163510"},"reference-count":53,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1371\/journal.pone.0324759","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T13:30:04Z","timestamp":1750858204000},"page":"e0324759","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["RETRACTED: Application of IRSA-BP neural network in diagnosing diabetes"],"prefix":"10.1371","volume":"20","author":[{"given":"Wan-Hua","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1318-8738","authenticated-orcid":true,"given":"Zi-Xun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"pone.0324759.ref001","unstructured":"A V K V, B S V. Machine learning applications in healthcare sector: an overview. 2021."},{"issue":"1","key":"pone.0324759.ref002","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41746-022-00592-y","article-title":"Machine learning for medical imaging: methodological failures and recommendations for the future","volume":"5","author":"G Varoquaux","year":"2022","journal-title":"NPJ Digit Med"},{"issue":"1","key":"pone.0324759.ref003","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","article-title":"Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis","volume":"4","author":"R Aggarwal","year":"2021","journal-title":"NPJ Digit Med"},{"key":"pone.0324759.ref004","doi-asserted-by":"crossref","first-page":"104077","DOI":"10.1016\/j.medengphy.2023.104077","article-title":"Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images","volume":"123","author":"LK Singh","year":"2024","journal-title":"Med Eng Phys"},{"key":"pone.0324759.ref005","doi-asserted-by":"crossref","first-page":"558913","DOI":"10.3389\/fpubh.2020.558913","article-title":"First, Do No Harm (Gone Wrong): Total-Scale Analysis of Medical Errors Scientific Literature","volume":"8","author":"AG Atanasov","year":"2020","journal-title":"Front Public Health"},{"issue":"3","key":"pone.0324759.ref006","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1136\/qshc.2007.023622","article-title":"The incidence and nature of in-hospital adverse events: a systematic review","volume":"17","author":"EN de Vries","year":"2008","journal-title":"Qual Saf Health Care"},{"issue":"8","key":"pone.0324759.ref007","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.3390\/jpm12081208","article-title":"MLP-PSO Hybrid Algorithm for Heart Disease Prediction","volume":"12","author":"A Al Bataineh","year":"2022","journal-title":"J Pers Med"},{"issue":"19","key":"pone.0324759.ref008","doi-asserted-by":"crossref","first-page":"27737","DOI":"10.1007\/s11042-022-12826-y","article-title":"Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images","volume":"81","author":"LK Singh","year":"2022","journal-title":"Multimed Tools Appl"},{"key":"pone.0324759.ref009","first-page":"008","article-title":"Data mining in clinical big data: the frequently used databases, steps, and methodological models","volume":"004","author":"WT Wu","year":"2021","journal-title":"Military Medical Research"},{"key":"pone.0324759.ref010","doi-asserted-by":"crossref","first-page":"100142","DOI":"10.1016\/j.cmpbup.2024.100142","article-title":"Advancing clinical decision support: The role of artificial intelligence across six domains","volume":"5","author":"M Khalifa","year":"2024","journal-title":"Computer Methods and Programs in Biomedicine Update"},{"issue":"12","key":"pone.0324759.ref011","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1038\/s41551-022-00898-y","article-title":"Shifting machine learning for healthcare from development to deployment and from models to data","volume":"6","author":"A Zhang","year":"2022","journal-title":"Nat Biomed Eng"},{"issue":"1","key":"pone.0324759.ref012","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/s10020-024-00824-9","article-title":"An update on chronic complications of diabetes mellitus: from molecular mechanisms to therapeutic strategies with a focus on metabolic memory","volume":"30","author":"T Yang","year":"2024","journal-title":"Mol Med"},{"issue":"1","key":"pone.0324759.ref013","doi-asserted-by":"crossref","first-page":"14790","DOI":"10.1038\/s41598-020-71908-9","article-title":"Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025","volume":"10","author":"X Lin","year":"2020","journal-title":"Sci Rep"},{"key":"pone.0324759.ref014","doi-asserted-by":"crossref","unstructured":"Sosenko JM, Krischer JP, Palmer JP. A risk score for type 1 diabetes derived from participants in the diabetes prevention trial-type 1 (DPT-1). In: 2007.","DOI":"10.2337\/dc07-1459"},{"issue":"8","key":"pone.0324759.ref015","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s11892-018-1020-6","article-title":"The Gut Microbiome as a Target for the Treatment of Type 2 Diabetes","volume":"18","author":"\u00d6 Aydin","year":"2018","journal-title":"Curr Diab Rep"},{"issue":"2","key":"pone.0324759.ref016","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1016\/j.fertnstert.2008.06.045","article-title":"Risk of gestational diabetes mellitus in women with polycystic ovary syndrome: a systematic review and a meta-analysis","volume":"92","author":"KA Toulis","year":"2009","journal-title":"Fertil Steril"},{"key":"pone.0324759.ref017","first-page":"593","article-title":"Maturity-onset diabetes of the young: an update","volume":"52","author":"K Olek","year":"2006","journal-title":"Clin Lab"},{"issue":"9","key":"pone.0324759.ref018","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.2337\/dc15-2171","article-title":"Global Estimates on the Number of People Blind or Visually Impaired by Diabetic Retinopathy: A Meta-analysis From 1990 to 2010","volume":"39","author":"JL Leasher","year":"2016","journal-title":"Diabetes Care"},{"issue":"5","key":"pone.0324759.ref019","doi-asserted-by":"crossref","first-page":"963","DOI":"10.2337\/dc17-1962","article-title":"Global economic burden of diabetes in adults: projections from 2015 to 2030","volume":"41","author":"C Bommer","year":"2018","journal-title":"Diabetes Care"},{"issue":"1","key":"pone.0324759.ref020","doi-asserted-by":"crossref","first-page":"2346","DOI":"10.1038\/s41598-023-29276-7","article-title":"Obesity and risk for its comorbidities diabetes, hypertension, and dyslipidemia in Japanese individuals aged 65 years","volume":"13","author":"T Yamada","year":"2023","journal-title":"Sci Rep"},{"issue":"1","key":"pone.0324759.ref021","doi-asserted-by":"crossref","first-page":"11981","DOI":"10.1038\/s41598-020-68771-z","article-title":"Early detection of type 2 diabetes mellitus using machine learning-based prediction models","volume":"10","author":"L Kopitar","year":"2020","journal-title":"Sci Rep"},{"issue":"1","key":"pone.0324759.ref022","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1038\/s41387-024-00324-z","article-title":"Optimization of diabetes prediction methods based on combinatorial balancing algorithm","volume":"14","author":"H Shao","year":"2024","journal-title":"Nutr Diabetes"},{"key":"pone.0324759.ref023","unstructured":"Kamble MTP, Patil ST. Diabetes detection using deep learning approach. 2016."},{"issue":"12","key":"pone.0324759.ref024","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.3390\/app9122555","article-title":"A method to detect type 1 diabetes based on physical activity measurements using a mobile device","volume":"9","author":"A Czmil","year":"2019","journal-title":"Appld Sci"},{"key":"pone.0324759.ref025","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/B978-0-12-819061-6.00014-8","article-title":"Diabetes prediction using artificial neural network","volume-title":"Deep Learning Techniques for Biomedical and Health Informatics","author":"N Pradhan","year":"2020"},{"key":"pone.0324759.ref026","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.procs.2020.03.065","article-title":"Diabetes diagnostic prediction using vector support machines","volume":"170","author":"A Viloria","year":"2020","journal-title":"Procedia Computer Science"},{"key":"pone.0324759.ref027","doi-asserted-by":"crossref","first-page":"106773","DOI":"10.1016\/j.cmpb.2022.106773","article-title":"Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective","volume":"220","author":"CC Olisah","year":"2022","journal-title":"Comput Methods Programs Biomed"},{"key":"pone.0324759.ref028","doi-asserted-by":"crossref","unstructured":"Khanam A, Masoodi FS. Early Detection of Type-2 Diabetes Mellitus using Machine Learning based Prediction Models. In: 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), 2024. 1398\u2013403. doi: 10.23919\/indiacom61295.2024.10498997","DOI":"10.23919\/INDIACom61295.2024.10498997"},{"key":"pone.0324759.ref029","doi-asserted-by":"crossref","first-page":"107658","DOI":"10.1016\/j.patcog.2020.107658","article-title":"Unified Cross-domain Classification via Geometric and Statistical Adaptations","volume":"110","author":"W Liu","year":"2021","journal-title":"Pattern Recognition"},{"key":"pone.0324759.ref030","doi-asserted-by":"crossref","first-page":"3","DOI":"10.3233\/IDA-1997-1102","article-title":"Data preprocessing and intelligent data analysis","volume":"1","author":"A FAMILI","year":"1997","journal-title":"Intelligent Data Analysis"},{"key":"pone.0324759.ref031","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.knosys.2013.11.015","article-title":"Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting","volume":"56","author":"A B C R","year":"2014","journal-title":"Knowledge-Based Systems"},{"issue":"3","key":"pone.0324759.ref032","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3390\/technologies9030052","article-title":"Effect of data scaling methods on machine learning algorithms and model performance","volume":"9","author":"M Ahsan","year":"2021","journal-title":"Technologies"},{"key":"pone.0324759.ref033","doi-asserted-by":"crossref","first-page":"116158","DOI":"10.1016\/j.eswa.2021.116158","article-title":"Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer","volume":"191","author":"L Abualigah","year":"2022","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"pone.0324759.ref034","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s11831-023-09990-1","article-title":"Reptile search algorithm: theory, variants, applications, and performance evaluation","volume":"31","author":"B Sasmal","year":"2024","journal-title":"Archives of Computational Methods in Engineering: State of the Art Reviews"},{"key":"pone.0324759.ref035","doi-asserted-by":"crossref","unstructured":"Nie C, Shi Y, Bin G, Huang J. Research of Analysis Method about Characteristics on Periodic, Chaotic and Stochastic Signal. In: 2006 8th international Conference on Signal Processing, 2006. doi: 10.1109\/icosp.2006.344469","DOI":"10.1109\/ICOSP.2006.344469"},{"issue":"4","key":"pone.0324759.ref036","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.7498\/aps.59.2298","article-title":"A new piecewise nonlinear chaotic map and its performance","volume":"59","author":"XF Zhang","year":"2010","journal-title":"Acta Phys Sin"},{"key":"pone.0324759.ref037","article-title":"Multi-objective fireworks optimization algorithm using elite opposition-based learning","author":"CW Xie","year":"2016","journal-title":"Acta Electronica Sinica"},{"key":"pone.0324759.ref038","doi-asserted-by":"crossref","unstructured":"Liu Q, Jia H, Wu D, Li N, Qi Q, Huang X. Hybrid Optimization Algorithm Based on Arithmetic and Golden Sine Algorithms for Constrained Engineering Problem. In: 2022 China Automation Congress (CAC), 2022. 157\u201362. doi: 10.1109\/cac57257.2022.10054988","DOI":"10.1109\/CAC57257.2022.10054988"},{"issue":"2","key":"pone.0324759.ref039","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s00521-016-2455-9","article-title":"A constrained optimization method based on BP neural network","volume":"29","author":"L Zhang","year":"2016","journal-title":"Neural Comput Applic"},{"key":"pone.0324759.ref040","article-title":"Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark","author":"SR Dubey","year":"2021"},{"issue":"1","key":"pone.0324759.ref041","doi-asserted-by":"crossref","first-page":"18757","DOI":"10.1038\/s41598-021-96723-8","article-title":"Universal activation function for machine learning","volume":"11","author":"B Yuen","year":"2021","journal-title":"Sci Rep"},{"key":"pone.0324759.ref042","article-title":"Understanding deep neural networks with rectified linear units","author":"R Arora","year":"2016"},{"key":"pone.0324759.ref043","doi-asserted-by":"crossref","first-page":"107250","DOI":"10.1016\/j.cie.2021.107250","article-title":"Aquila Optimizer: A novel meta-heuristic optimization algorithm","volume":"157","author":"L Abualigah","year":"2021","journal-title":"Computers & Industrial Engineering"},{"key":"pone.0324759.ref044","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.eswa.2020.113377","article-title":"Marine Predators Algorithm: A nature-inspired metaheuristic","volume":"152","author":"A Faramarzi","year":"2020","journal-title":"Expert Systems with Applications"},{"key":"pone.0324759.ref045","volume-title":"Scientific Computing with MATLAB","author":"FAUSTO ALFIOQUARTERONI","year":"2024"},{"issue":"10","key":"pone.0324759.ref046","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1016\/j.camwa.2013.09.006","article-title":"Data partition methodology for validation of predictive models","volume":"66","author":"RE Morrison","year":"2013","journal-title":"Computers Mathematics with Applications"},{"key":"pone.0324759.ref047","doi-asserted-by":"crossref","unstructured":"Obaid HS, Dheyab SA, Sabry SS. The Impact of Data Pre-Processing Techniques and Dimensionality Reduction on the Accuracy of Machine Learning. In: 2019 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON), 2019. 279\u201383. doi: 10.1109\/iemeconx.2019.8877011","DOI":"10.1109\/IEMECONX.2019.8877011"},{"key":"pone.0324759.ref048","doi-asserted-by":"crossref","first-page":"101794","DOI":"10.1016\/j.datak.2020.101794","article-title":"Content-based Node2Vec for representation of papers in the scientific literature","volume":"127","author":"B Kazemi","year":"2020","journal-title":"Data & Knowledge Engineering"},{"key":"pone.0324759.ref049","doi-asserted-by":"crossref","unstructured":"Arias-Duart A, Mariotti E, Garcia-Gasulla D, Alonso-Moral JM. A Confusion Matrix for Evaluating Feature Attribution Methods. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023. 3709\u201314. doi: 10.1109\/cvprw59228.2023.00380","DOI":"10.1109\/CVPRW59228.2023.00380"},{"issue":"1","key":"pone.0324759.ref050","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s44352-025-00006-1","article-title":"Deep learning imaging analysis to identify bacterial metabolic states associated with carcinogen production","volume":"2","author":"M Orouskhani","year":"2025","journal-title":"Discov Imaging"},{"issue":"1","key":"pone.0324759.ref051","doi-asserted-by":"crossref","first-page":"15724","DOI":"10.1038\/s41598-024-66611-y","article-title":"Author correction: evaluation metrics and statistical tests for machine learning","volume":"14","author":"O Rainio","year":"2024","journal-title":"Sci Rep"},{"issue":"3","key":"pone.0324759.ref052","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1007\/s10994-021-05964-1","article-title":"F*: an interpretable transformation of the F-measure","volume":"110","author":"DJ Hand","year":"2021","journal-title":"Mach Learn"},{"issue":"1","key":"pone.0324759.ref053","doi-asserted-by":"crossref","first-page":"37","DOI":"10.4018\/IJISMD.2020010103","article-title":"Multimodal biometric based on fusion of ridge features with minutiae features and face features","volume":"11","author":"LK Singh","year":"2020","journal-title":"Int J Informat Syst Model Design"}],"updated-by":[{"DOI":"10.1371\/journal.pone.0354316","type":"retraction","label":"Retraction","source":"publisher","updated":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000}},{"DOI":"10.1371\/journal.pone.0354316","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"record-id":"72880"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0324759","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T01:13:16Z","timestamp":1784769196000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0324759"}},"subtitle":[],"editor":[{"given":"Sheikh Arslan","family":"Sehgal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,6,25]]},"references-count":53,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6,25]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0324759","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,25]]}}}