Affiliations: [a] Department of Information Technology, St. Xavier’s Catholic College of Engineering, Chunkankadai, Tamil Nadu, India
| [b] Department of Computer Science and Engineering, Ponjesly College of Engineering, India
Correspondence:
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Corresponding author. G. Geo Jenefer, Assistant Professor, Department of Information Technology, St. Xavier’s Catholic College of Engineering, Chunkankadai, Tamil Nadu, India. E-mail: [email protected].
Abstract: 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.
