Abstract
Diabetes is one of the dangerous diseases that bring about abnormalities in blood sugar levels. Early treatment can mitigate the negative consequences of this disease. Machine learning algorithms can be leveraged to predict this disease at an early stage. In this study, a soft voting ensemble classifier approach combining random forest, AdaBoost and gradient boost algorithms is adopted to predict diabetes with the highest possible accuracy. The proposed method was tested on a publicly available dataset. The proposed approach predicted diabetes with 100% accuracy. As a result of the experiments conducted within the scope of the study, polyuria and polydipsia variables were found to be the most significant risk factors for this disease. The suggested approach outperformed similar studies in the literature.
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