{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T16:19:13Z","timestamp":1790093953468,"version":"4.0.1"},"reference-count":32,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:00:00Z","timestamp":1719878400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanxi PatentTransformation Special Programs","award":["No.202302012"],"award-info":[{"award-number":["No.202302012"]}]},{"name":"BasicResearch Program (Free Exploration) of Shanxi Province","award":["No.20210302123334"],"award-info":[{"award-number":["No.20210302123334"]}]},{"name":"Taiyuan Normal University Achievement Transformation and TechnologyTransfer Base","award":["No.2023P003"],"award-info":[{"award-number":["No.2023P003"]}]},{"name":"The Humanities and Social Sciences Research Foundation of the Ministry of Education","award":["No.23YJAZH118"],"award-info":[{"award-number":["No.23YJAZH118"]}]},{"name":"Foundation Research Programme of Shanxi Province","award":["No. 202103021224289"],"award-info":[{"award-number":["No. 202103021224289"]}]},{"name":"Shanxi PatentTransformation Special Programs","award":["No.202302009"],"award-info":[{"award-number":["No.202302009"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Diabetes is a chronic disease, which is characterized by abnormally high blood sugar levels. It may affect various organs and tissues, and even lead to life-threatening complications. Accurate prediction of diabetes can significantly reduce its incidence. However, the current prediction methods struggle to accurately capture the essential characteristics of nonlinear data, and the black-box nature of these methods hampers its clinical application. To address these challenges, we propose KCCAM_DNN, a diabetes prediction method that integrates Kendall\u2019s correlation coefficient and an attention mechanism within a deep neural network. In the KCCAM_DNN, Kendall\u2019s correlation coefficient is initially employed for feature selection, which effectively filters out key features influencing diabetes prediction. For missing values in the data, polynomial regression is utilized for imputation, ensuring data completeness. Subsequently, we construct a deep neural network (KCCAM_DNN) based on the self-attention mechanism, which assigns greater weight to crucial features affecting diabetes and enhances the model\u2019s predictive performance. Finally, we employ the SHAP model to analyze the impact of each feature on diabetes prediction, augmenting the model\u2019s interpretability. Experimental results show that KCCAM_DNN exhibits superior performance on both PIMA Indian and LMCH diabetes datasets, achieving test accuracies of 99.090% and 99.333%, respectively, approximately 2% higher than the best existing method. These results suggest that KCCAM_DNN is proficient in diabetes prediction, providing a foundation for informed decision-making in the diagnosis and prevention of diabetes.<\/jats:p>","DOI":"10.1371\/journal.pone.0306090","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T14:54:51Z","timestamp":1719932091000},"page":"e0306090","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":11,"title":["A deep neural network prediction method for diabetes based on Kendall\u2019s correlation coefficient and attention mechanism"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5867-8901","authenticated-orcid":true,"given":"Xiaobo","family":"Qi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0555-2480","authenticated-orcid":true,"given":"Yachen","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifang","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"issue":"3","key":"pone.0306090.ref001","article-title":"Retinopathy and Risk of Kidney Disease in Persons With Diabetes[J]","volume":"2021","author":"J Hong","journal-title":"Kidney Medicine"},{"key":"pone.0306090.ref002","volume-title":"IDF DIABETES ATLAS","author":"IDF Diabetes Atlas 10th edition scientific committee","year":"2021","edition":"10"},{"key":"pone.0306090.ref003","doi-asserted-by":"crossref","first-page":"2741","DOI":"10.1007\/s00125-021-05571-8","article-title":"Type 1 diabetes in 2017: global estimates of incident and prevalent cases in children and adults","volume":"64","author":"A. Green","year":"2021","journal-title":"Diabetologia"},{"issue":"1","key":"pone.0306090.ref004","doi-asserted-by":"crossref","first-page":"41","DOI":"10.33633\/jcta.v1i1.9190","article-title":"Dataset and Feature Analysis for Diabetes Mellitus Classification using Random Forest[J]","volume":"1","author":"F Mustofa","year":"2023","journal-title":"Journal of Computing Theories and Applications"},{"key":"pone.0306090.ref005","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1201\/9781003309451-12","volume-title":"Computational Intelligence in Medical Decision Making and Diagnosis","author":"Nitin Jaglal Untwal","year":"2023"},{"key":"pone.0306090.ref006","article-title":"Application of Logistic Regression Model in Prediction of Early Diabetes Across United States[J]","author":"I Olufemi","year":"2023","journal-title":"Researchgate"},{"key":"pone.0306090.ref007","doi-asserted-by":"crossref","unstructured":"Sivaranjani S, Ananya S, Aravinth J, et al. Diabetes prediction using machine learning algorithms with feature selection and dimensionality reduction[C]. 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2021,1:141-146.","DOI":"10.1109\/ICACCS51430.2021.9441935"},{"key":"pone.0306090.ref008","doi-asserted-by":"crossref","first-page":"76516","DOI":"10.1109\/ACCESS.2020.2989857","article-title":"Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers[J]","volume":"8","author":"M K Hasan","year":"2020","journal-title":"IEEE Access"},{"key":"pone.0306090.ref009","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/6611091","article-title":"Blood glucose level prediction of diabetic type 1 patients using nonlinear autoregressive neural networks[J]","volume":"2021","author":"M Asad","year":"2021","journal-title":"Journal of Healthcare Engineering"},{"issue":"3","key":"pone.0306090.ref010","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.11591\/eei.v12i3.4412","article-title":"Comparative analysis of predictive machine learning algorithms for diabetes mellitus[J]","volume":"12","author":"Kirti Kangra","year":"2023","journal-title":"Bulletin of Electrical Engineering and Informatics"},{"issue":"1","key":"pone.0306090.ref011","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s44196-023-00184-y","article-title":"An ensemble of Light Gradient Boosting Machine and adaptive boosting for prediction of type-2 diabetes[J]","volume":"16","author":"M J Sai","year":"2023","journal-title":"International Journal of Computational Intelligence Systems"},{"key":"pone.0306090.ref012","doi-asserted-by":"crossref","first-page":"829519","DOI":"10.3389\/fpubh.2022.829519","article-title":"A classification algorithm-based hybrid diabetes prediction model[J]","volume":"10","author":"M O Edeh","year":"2022","journal-title":"Frontiers in Public Health"},{"issue":"2","key":"pone.0306090.ref013","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.32604\/iasc.2023.028257","article-title":"Chi-square and PCA based feature selection for diabetes detection with ensemble classifier[J]","volume":"36","author":"V Rupapara","year":"2023","journal-title":"Intell. Autom. Soft Comput"},{"issue":"1","key":"pone.0306090.ref014","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12967-022-03349-z","article-title":"Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome:Tehran Cardio-metabolic Genetics Study[J]","volume":"20","author":"M Akbarzadeh","year":"2022","journal-title":"Journal of Translational Medicine"},{"key":"pone.0306090.ref015","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[J]","volume":"220","author":"Chollette C Olisah","year":"2022","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"4","key":"pone.0306090.ref016","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.icte.2021.02.004","article-title":"A comparison of machine learning algorithms for diabetes prediction[J]","volume":"7","author":"J J Khanam","year":"2021","journal-title":"Ict Express"},{"issue":"6","key":"pone.0306090.ref017","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1177\/19322968221092785","article-title":"Long-term prediction of blood glucose levels in type 1 diabetes using a cnn-lstm-based deep neural network[J]","volume":"17","author":"M Jaloli","year":"2023","journal-title":"Journal of diabetes science and technology"},{"issue":"1","key":"pone.0306090.ref018","doi-asserted-by":"crossref","first-page":"61","DOI":"10.18280\/ria.360107","article-title":"TCP\/UDP-Based Exploitation DDoS Attacks Detection Using AI Classification Algorithms with Common Uncorrelated Feature Subset Selected by Pearson, Spearman and Kendall Correlation Methods[J]","volume":"36","author":"Kishore Babu Dasari","year":"2022","journal-title":"Revue d\u2019Intelligence Artificielle"},{"issue":"1","key":"pone.0306090.ref019","first-page":"9","article-title":"Missing data imputation: focusing on single imputation[J]","volume":"4","author":"Zhongheng Zhang","year":"2016","journal-title":"Ann Transl Med"},{"issue":"3","key":"pone.0306090.ref020","first-page":"227","article-title":"Multiple imputation of missing values: update[J]","volume":"5","author":"Patrick Royston","year":"2005","journal-title":"Stata Journal"},{"issue":"1","key":"pone.0306090.ref021","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12874-020-01080-1","article-title":"Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction[J]","volume":"20","author":"S Hong","year":"2020","journal-title":"BMC medical research methodology"},{"key":"pone.0306090.ref022","doi-asserted-by":"crossref","first-page":"102232","DOI":"10.1109\/ACCESS.2019.2929866","article-title":"DMP_MI: an effective diabetes mellitus classification algorithm on imbalanced data with missing values[J]","volume":"7","author":"Q Wang","year":"2019","journal-title":"IEEE access"},{"key":"pone.0306090.ref023","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/BF02457822","article-title":"Review of neural network applications in medical imaging and signal processing[J]","volume":"30","author":"A S Miller","year":"1992","journal-title":"Medical and Biological Engineering and Computing"},{"key":"pone.0306090.ref024","unstructured":"Jung B, Mukuta Y, Harada T. Grouped self-attention mechanism for a memory-efficient Transformer[J]. 2022. https:\/\/doi.org\/10.48550\/arXiv.2210.00440"},{"key":"pone.0306090.ref025","doi-asserted-by":"crossref","first-page":"105946","DOI":"10.1016\/j.asoc.2019.105946","article-title":"Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis[J]","volume":"88","author":"M Wang","year":"2020","journal-title":"Applied Soft Computing"},{"key":"pone.0306090.ref026","doi-asserted-by":"crossref","first-page":"7941","DOI":"10.1007\/s00500-018-3424-2","article-title":"An efficient hybrid multilayer perceptron neural network with grasshopper optimization[J]","volume":"23","author":"A A Heidari","year":"2019","journal-title":"Soft Computing"},{"key":"pone.0306090.ref027","unstructured":"Lundberg S M, Lee S I. A Unified Approach to Interpreting Model Predictions[C]. Proceedings of the 31st International Conference on Neural Information Processing Systems(NIPS\u201917), 2017, 31:4768-4777. https:\/\/doi.org\/10.48550\/arXiv.1705.07874"},{"key":"pone.0306090.ref028","article-title":"pAtbP-EnC: identifying anti-tubercular peptides using multi-feature representation and genetic algorithm based deep ensemble model[J]","author":"S Akbar","year":"2023","journal-title":"IEEE Access"},{"key":"pone.0306090.ref029","doi-asserted-by":"crossref","first-page":"102860","DOI":"10.1016\/j.artmed.2024.102860","article-title":"iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks[J]","volume":"151","author":"S Akbar","year":"2024","journal-title":"Artificial Intelligence in Medicine"},{"key":"pone.0306090.ref030","unstructured":"Smith J W, Everhart J E, Dickson W C, et al. Using the ADAP learning algorithm to forecast the onset of diabetes mellitus[C]. Proceedings of the annual symposium on computer application in medical care. American Medical Informatics Association,1988:261."},{"key":"pone.0306090.ref031","doi-asserted-by":"crossref","unstructured":"Ahmed U, Li C. Machine Learning for Diabetes Prediction[C]\/\/2021 International Conference on Information and Communication Technology Convergence (ICTC). IEEE,2021:16-19.","DOI":"10.1109\/ICTC52510.2021.9621066"},{"key":"pone.0306090.ref032","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1613\/jair.614","article-title":"Popular ensemble methods: An empirical study[J]","volume":"11","author":"D Opitz","year":"1999","journal-title":"Journal of artificial intelligence research"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0306090","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T14:55:34Z","timestamp":1719932134000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0306090"}},"subtitle":[],"editor":[{"given":"Shahid","family":"Akbar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,7,2]]},"references-count":32,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7,2]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0306090","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,2]]}}}