An Ensemble of Light Gradient Boosting Machine and Adaptive Boosting for Prediction of Type-2 Diabetes
International Journal of Computational Intelligence Systems, ISSN: 1875-6883, Vol: 16, Issue: 1
2023
- 29Citations
- 88Captures
- 2Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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1Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX, USA; 2Novo Nordisk Inc, Plainsboro, NJ, USA; 3Department of Biostatistics, University of Michigan,
Article Description
Machine learning helps construct predictive models in clinical data analysis, predicting stock prices, picture recognition, financial 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 fivefold 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.
Bibliographic Details
Springer Science and Business Media LLC
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