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http://dx.doi.org/10.6109/jkiice.2021.25.10.1296

Diabetes prediction mechanism using machine learning model based on patient IQR outlier and correlation coefficient  

Jung, Juho (Applied Artifical Intelligence, Sungkyunkwan University)
Lee, Naeun (Applied Artifical Intelligence, Sungkyunkwan University)
Kim, Sumin (Applied Artifical Intelligence, Sungkyunkwan University)
Seo, Gaeun (Applied Artifical Intelligence, Sungkyunkwan University)
Oh, Hayoung (College of Computing and Informatics, Sungkyunkwan University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.10, 2021 , pp. 1296-1301 More about this Journal
Abstract
With the recent increase in diabetes incidence worldwide, research has been conducted to predict diabetes through various machine learning and deep learning technologies. In this work, we present a model for predicting diabetes using machine learning techniques with German Frankfurt Hospital data. We apply outlier handling using Interquartile Range (IQR) techniques and Pearson correlation and compare model-specific diabetes prediction performance with Decision Tree, Random Forest, Knn (k-nearest neighbor), SVM (support vector machine), Bayesian Network, ensemble techniques XGBoost, Voting, and Stacking. As a result of the study, the XGBoost technique showed the best performance with 97% accuracy on top of the various scenarios. Therefore, this study is meaningful in that the model can be used to accurately predict and prevent diabetes prevalent in modern society.
Keywords
Stacking; Ensemble; Diabetes prediction; Machine learning; Interquartile range (IQR);
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