JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

Original Article
Diabetes prediction utilizing soft voting classifier
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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Volume 2, Issue 1, 2024
Page : 31-34