{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:14:13Z","timestamp":1785338053114,"version":"3.55.0"},"reference-count":43,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2022,7,31]],"date-time":"2022-07-31T00:00:00Z","timestamp":1659225600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Chronic Diseases and Translational Medicine"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Being able to predict with confidence the early onset of type 2 diabetes from a suite of signs and symptoms (features) displayed by potential sufferers is desirable to commence treatment promptly. Late or inconclusive diagnosis can result in more serious health consequences for sufferers and higher costs for health care services in the long run.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A novel integrated methodology is proposed involving correlation, statistical analysis, machine learning, multi\u2010<jats:italic>K<\/jats:italic>\u2010fold cross\u2010validation, and confusion matrices to provide a reliable classification of diabetes\u2010positive and \u2010negative individuals from a substantial suite of features. The method also identifies the relative influence of each feature on the diabetes diagnosis and highlights the most important ones. Ten statistical and machine learning methods are utilized to conduct the analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A published data set involving 520 individuals (Sylthet Diabetes Hospital, Bangladesh) is modeled revealing that a support vector classifier generates the most accurate early\u2010onset type 2 diabetes status predictions with just 11 misclassifications (2.1% error). Polydipsia and polyuria are among the most influential features, whereas obesity and age are assigned low weights by the prediction models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed methodology can rapidly predict early\u2010onset type 2 diabetes with high confidence while providing valuable insight into the key influential features involved in such predictions.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cdt3.39","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T06:04:47Z","timestamp":1659333887000},"page":"281-295","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Integrated statistical and machine learning analysis provides insight into key influencing symptoms for distinguishing early\u2010onset type 2 diabetes"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3202-4069","authenticated-orcid":false,"given":"David A.","family":"Wood","sequence":"first","affiliation":[{"name":"DWA Energy Limited Lincoln UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,7,31]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.2337\/dc21-S002"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0242415"},{"key":"e_1_2_11_4_1","unstructured":"International Diabetes Federation. IDF Diabetes Atlas 10th Edition;\u00a02021. Accessed April 15 2022.https:\/\/diabetesatlas.org\/atlas\/tenth-edition\/"},{"key":"e_1_2_11_5_1","unstructured":"Diabetes Australia. Failure to detect type 2 diabetes early costing $700 million per year.\u00a02018. Accessed April 15 2022.https:\/\/www.diabetesaustralia.com.au\/mediarelease\/failure-to-detect-type-2-diabetes-early-costing-700-million-per-year\/"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.2337\/dc14-2459"},{"issue":"2","key":"e_1_2_11_7_1","first-page":"65","article-title":"Effect of diet on type 2 diabetes mellitus: a review","volume":"11","author":"Sami W","year":"2017","journal-title":"Int J Health Sci"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-68771-z"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0235376"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/jdi.12549"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18147346"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13098-021-00767-9"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-8798-2_12"},{"key":"e_1_2_11_14_1","unstructured":"UCI Dataset. Early stage diabetes risk prediction dataset. UCI Machine Learning Repository [http:\/\/archive.ics.uci.edu\/ml]. Irvine CA: University of California School of Information and Computer Science.\u00a02020. Accessed April 15 2022.https:\/\/archive.ics.uci.edu\/ml\/datasets\/Early%2Bstage%2Bdiabetes%2Brisk%2Bprediction%2Bdataset.#"},{"issue":"227","key":"e_1_2_11_15_1","first-page":"357","article-title":"Application of the logistic function to bio\u2010assay","volume":"39","author":"Berkson J","year":"1944","journal-title":"J Am Stat Assoc"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.25.11.1999"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpbup.2021.100032"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1751-5823.2001.tb00465.x"},{"key":"e_1_2_11_19_1","first-page":"1167","volume-title":"Intelligence in Big Data Technologies\u2014Beyond the Hype. Advances in Intelligent Systems and Computing","author":"Theresa JS","year":"2021"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-1809.1936.tb02137.x"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJAPR.2016.079050"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-018-0940-7"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"e_1_2_11_24_1","doi-asserted-by":"crossref","unstructured":"VijayanVV AnjaliC. Prediction and diagnosis of diabetes mellitus\u2014a machine learning approach.2015 IEEE Recent Advances in Intelligent Computational Systems.2015. pp. 122\u2010127.doi:10.1109\/RAICS.2015.7488400","DOI":"10.1109\/RAICS.2015.7488400"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116251"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2016-013336"},{"key":"e_1_2_11_27_1","doi-asserted-by":"crossref","unstructured":"FixE HodgesJLJr.Discriminatory analysis nonparametric discrimination: consistency properties. USAF School of Aviation Medicine. Technical Report 4;1951. p. 21.","DOI":"10.1037\/e471672008-001"},{"issue":"26","key":"e_1_2_11_28_1","article-title":"K\u2010Nearest Neighbor learning based diabetes mellitus prediction and analysis for eHealth services","volume":"7","author":"Sarkar IH","year":"2019","journal-title":"EAI End Trans Scal Inf Sys"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0042519"},{"key":"e_1_2_11_30_1","doi-asserted-by":"publisher","DOI":"10.1142\/S1793962321500124"},{"key":"e_1_2_11_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.709601"},{"key":"e_1_2_11_32_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-021-01471-4"},{"key":"e_1_2_11_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"e_1_2_11_34_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0219636"},{"key":"e_1_2_11_35_1","doi-asserted-by":"crossref","unstructured":"ChenT GuestrinC.XGBoost: a scalable tree boosting system. In:KrishnapuramB ShahM SmolaAJ et al. eds.Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining San Francisco CA USA. August 13\u201017 2016. pp.785\u2010794.doi:10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_11_36_1","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare8030247"},{"key":"e_1_2_11_37_1","unstructured":"SciKit Learn. Supervised and unsupervised machine learning models in Python.\u00a02022. April 15 2022.https:\/\/scikit-learn.org\/stable\/"},{"key":"e_1_2_11_38_1","doi-asserted-by":"publisher","DOI":"10.3102\/00346543045004543"},{"key":"e_1_2_11_39_1","first-page":"769","article-title":"Concentration and dependency ratios (published 1909 in Italian)","volume":"87","author":"Gini C","year":"1997","journal-title":"Riv\u00a0di Polit\u00a0Econ"},{"key":"e_1_2_11_40_1","unstructured":"SciKit Learn. GridSearchCV: Exhaustive search over specified parameter values for an estimator in Python.\u00a02022. April 15 2022.https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.GridSearchCV.html"},{"key":"e_1_2_11_41_1","unstructured":"SciKit Learn. Bayesian optimization of hyperparameters in Python.\u00a02022. April 15 2022.https:\/\/scikit-optimize.github.io\/stable\/modules\/generated\/skopt.BayesSearchCV.html"},{"key":"e_1_2_11_42_1","unstructured":"SciKit Learn. Cross\u2010validation: evaluating estimator performance.\u00a02022. April 15 2022.https:\/\/scikit-learn.org\/stable\/modules\/cross_validation.html"},{"key":"e_1_2_11_43_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.1894.0003"},{"key":"e_1_2_11_44_1","doi-asserted-by":"publisher","DOI":"10.2307\/2532051"}],"container-title":["Chronic Diseases and Translational Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cdt3.39","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/cdt3.39","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cdt3.39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T05:08:39Z","timestamp":1706677719000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cdt3.39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,31]]},"references-count":43,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["10.1002\/cdt3.39"],"URL":"https:\/\/doi.org\/10.1002\/cdt3.39","archive":["Portico"],"relation":{},"ISSN":["2589-0514","2589-0514"],"issn-type":[{"value":"2589-0514","type":"print"},{"value":"2589-0514","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,31]]},"assertion":[{"value":"2022-04-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-07-07","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-07-31","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}