An ensemble of light gradient boosting machine and adaptive boosting for prediction of type-2 diabetes

Sai Mj, Chettri P, Panigrahi R, Garg A, Bhoi Ak, Barsocchi P

k-NN  Light GBM  Naive Bayes  Random forest  Diabetes detection 

Machine learning helps construct predictive models in clinical data analysis, predicting stock prices, picture recognition, fnancial modelling, disease prediction, and diagnostics. This paper proposes machine learning ensemble algorithms to forecast diabetes. The ensemble combines k-NN, Naive Bayes (Gaussian), Random Forest (RF), Adaboost, and a recently designed Light Gradient Boosting Machine. The proposed ensembles inherit detection ability of LightGBM to boost accuracy. Under fvefold cross-validation, the proposed ensemble models perform better than other recent models. The k-NN, Adaboost, and LightGBM jointly achieve 90.76% detection accuracy. The receiver operating curve analysis shows that k -NN, RF, and LightGBM successfully solve class imbalance issue of the underlying dataset.

Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS (ONLINE), vol. 16