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The lifestyle disorder has been a prime factor in the imbalance of glucose levels in the human body, causing chronic diseases like diabetes. It is one of the major metabolic disorders. This is instigated by a disorder of secretion of a vital hormone that is called insulin and has its roots in the medical history of ancestors or both. Diabetes is of the major following types: type-I diabetes (Diabetes Mellitus-I) and type-II diabetes (Diabetes Mellitus-II). The care and quality of life for patients may be enhanced if we could anticipate the course of diabetes early on. From our literature review, we found that usually Random Forest and Decision Tree give good results. So, we take Random Forest, Decision Tree, Logit model, KNN, Naive Bayes Gaussian, and Linear SVM in the Pima Indians Diabetes Dataset. The outcomes demonstrate that the ensemble approach, logistic regression, and random forest all provide the output with excellent accuracy.

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Evaluating the Top Machine Learning Classifiers Used in Diabetes Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • Manmath Nath Das,
  • Rajesh Kumar Ojha,
  • Jyotsnarani Tripathy,
  • Gopikrishna Panda

摘要

The lifestyle disorder has been a prime factor in the imbalance of glucose levels in the human body, causing chronic diseases like diabetes. It is one of the major metabolic disorders. This is instigated by a disorder of secretion of a vital hormone that is called insulin and has its roots in the medical history of ancestors or both. Diabetes is of the major following types: type-I diabetes (Diabetes Mellitus-I) and type-II diabetes (Diabetes Mellitus-II). The care and quality of life for patients may be enhanced if we could anticipate the course of diabetes early on. From our literature review, we found that usually Random Forest and Decision Tree give good results. So, we take Random Forest, Decision Tree, Logit model, KNN, Naive Bayes Gaussian, and Linear SVM in the Pima Indians Diabetes Dataset. The outcomes demonstrate that the ensemble approach, logistic regression, and random forest all provide the output with excellent accuracy.