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Diabetes Prediction Using Derived Features and ensembling of Boosting Classifiers

Computers, Materials & Continua 2022年第10期73卷 2013-2033页

作者： R.Rajkamal Anitha Karthi Xiao-Zhi Gao School of Computing SRM Institute of Science and TechnologyKattankulathurChennaiIndia School of Computing Bharat Institute of Higher Education and ResearchChennaiIndia School of Computing University of Eastern FinlandKuopioFinland

Diabetes is increasing commonly in people’s daily life and represents an extraordinary threat to human *** Learning(ML)in the healthcare industry has recently made *** ML models are developed around different datasets for diabetic *** is essential for ML models to predict diabetes *** informative features of the dataset are vital to determine the capability factors of the model in the prediction of *** engineering(FE)is the way of taking forward in yielding highly informative *** Indian Diabetes Dataset(PIDD)is used in this work,and the impact of informative features in ML models is experimented with and analyzed for the prediction of *** values(MV)and the effect of the imputation process in the data distribution of each feature are *** importance and partial dependence are carried out extensively and the results revealed that Glucose(GLUC),Body Mass Index(BMI),and Insulin(INS)are highly informative *** features are obtained for BMI and INS to add more information with its raw *** ensemble classifier with an ensemble of AdaBoost(AB)and XGBoost(XB)is considered for the impact analysis of the proposed FE *** ensemble model performs well for the inclusion of derived features provided the high Diagnostics Odds Ratio(DOR)of *** shows a high margin of 8.2%when compared with the ensemble model with no derived features(DOR=96.306)included in the *** inclusion of derived features with the FE approach of the current state-of-the-art made the ensemble model performs well with Sensitivity(0.793),Specificity(0.945),DOR(79.517),and False Omission Rate(0.090)which further improves the state-of-the-art results.

关键词： Diabetes prediction feature engineering highly informative features ML models ensembling models

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