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Diabetes mellitus has been affecting people worldwide for decades. It frequently results in harmful health conditions like kidney troubles, heart attacks, nervous system disruption, and vision problems, among others. The majority of those affected by the illness are between the ages of 25 and 74. If diabetes is not identified and treated in a timely way, it may be the primary cause of most issues. Undiagnosed or untreated diabetes can lead to several complications. It is crucial to anticipate and identify such a fatal illness. We developed a deep neural network-based diabetic prediction model for the study that was done. We used two- and four-fold cross-validation in our experiments. In comparison to a few popular ML classification algorithms, including LR, SVM, XGBoost, DT, and RF our DNN model has a 98.45% accuracy rate, which is a very good result.

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Deep Neural Networks for Early Diabetes Mellitus Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • Rella Usha Rani,
  • S. V. Suryanarayana,
  • K. Kavitha,
  • Preeti Nutipalli

摘要

Diabetes mellitus has been affecting people worldwide for decades. It frequently results in harmful health conditions like kidney troubles, heart attacks, nervous system disruption, and vision problems, among others. The majority of those affected by the illness are between the ages of 25 and 74. If diabetes is not identified and treated in a timely way, it may be the primary cause of most issues. Undiagnosed or untreated diabetes can lead to several complications. It is crucial to anticipate and identify such a fatal illness. We developed a deep neural network-based diabetic prediction model for the study that was done. We used two- and four-fold cross-validation in our experiments. In comparison to a few popular ML classification algorithms, including LR, SVM, XGBoost, DT, and RF our DNN model has a 98.45% accuracy rate, which is a very good result.