{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:10:03Z","timestamp":1777705803938,"version":"3.51.4"},"reference-count":18,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:p>Globally, diabetes directly causes 1.5 million fatalities each year. It is necessary to predict such diseases at an earlier stage and cure them. Since modern healthcare data comprises huge amounts of information, it is tough to process such data in conventional databases. Previously, various machine learning (ML) algorithms were used to predict diabetics, and their performance was evaluated. But still, those existing algorithms result in poor accuracy and performance.This work proposes a FOCB (Firefly Optimization-based CatBoost) classifier for predicting diabetes. The PIMA Indian diabetic dataset has been taken as the input dataset. The proposed FOCB algorithm has been compared with various machine learning algorithms. From the results, we can see that the FOCB classifier gives the best accuracy of 96% with improved performance. The proposed system has been compared with other FO-based machine learning algorithms like NB, KNN, RF, AB, GB, XGB, CNN, DBN, and CB, and it has been proven that CB based on FO produces better accuracy with less hamming loss.<\/jats:p>","DOI":"10.3233\/jifs-223105","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T14:43:55Z","timestamp":1680619435000},"page":"9943-9954","source":"Crossref","is-referenced-by-count":2,"title":["Diabetes disease prediction using firefly optimization-based cat-boost classifier in big data analytics"],"prefix":"10.1177","volume":"44","author":[{"given":"G.","family":"Geo Jenefer","sequence":"first","affiliation":[{"name":"Department of Information Technology, St. Xavier\u2019s Catholic College of Engineering, Chunkankadai, Tamil Nadu, India"}]},{"given":"A.J.","family":"Deepa","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Ponjesly College of Engineering, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-223105_ref5","doi-asserted-by":"crossref","first-page":"8869","DOI":"10.1109\/ACCESS.2017.2694446","article-title":"Disease prediction by ML over big data from healthcare communities","volume":"5","author":"Chen","year":"2017","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-223105_ref6","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.procs.2015.04.069","article-title":"Predictive methodology for diabetic data analysis in big data","volume":"50","author":"Eswari","year":"2015","journal-title":"Procedia Computer Science"},{"key":"10.3233\/JIFS-223105_ref7","doi-asserted-by":"crossref","unstructured":"Bhat V.H. , Rao P.G. , et al., An efficient prediction model for diabetic database using soft computing techniques, In International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing (2009), pp. 328\u2013335. 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Dr. , A Cognitive Survey on Big Data Analytics in Predicting Chronic Diseases, Journal of Computational Information Systems 14(6) (2018)."},{"issue":"1","key":"10.3233\/JIFS-223105_ref15","doi-asserted-by":"crossref","first-page":"1","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":"Kopitar","year":"2020","journal-title":"Scientific reports"},{"key":"10.3233\/JIFS-223105_ref16","doi-asserted-by":"crossref","first-page":"100204","DOI":"10.1016\/j.imu.2019.100204","article-title":"Baig, A. 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