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- W4383504071 abstract "Worldwide, diabetes mellitus (DM) is one of the primary causes of illness and death. Diabetes has a well-established hereditary link, which is widely acknowledged. Diabetes is a disease that occurs when the blood glucose level in the body is too high. Diabetes can result in serious cardiovascular problems. According to the WHO, there are 3.7 million fatalities by the age of 70, and cardiovascular disease is increasing day by day which leads to a high death rate ( https://www.cdc.gov/diabetes/basics/diabetes.html [16]). Therefore, the detection of diabetes at an early stage can help patients’ lives. Various research is going under the healthcare sector in the prediction of disease in the field of data mining. Data mining techniques are frequently utilized in early disease diagnosis. In this paper, five diabetes datasets have been used and the main objective is to compare the various machine learning algorithms for detecting and classifying diabetes. The algorithms used for classification are Random Forest, Logistic Regression, Naïve Bayes, XGBoost, and the proposed ensemble approach. The proposed ensemble approach provided better accuracy as compared with individual classification algorithms." @default.
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- W4383504071 date "2023-01-01" @default.
- W4383504071 modified "2023-09-24" @default.
- W4383504071 title "A Study on Transcontinental Diabetes Datasets Using a Soft-Voting Ensemble Learning Approach" @default.
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- W4383504071 doi "https://doi.org/10.1007/978-981-99-1983-3_9" @default.
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