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This paper expresses an innovative method of data science architecture for enhanced diabetes prediction, embedding comprehensive mathematical framework, statistical feature engineering, and machine learning [1]. Utilizing the Pima Indian Diabetes dataset, we design and extract key features that drive prediction - Insulin-to-Glucose Ratio (IGR), Diabetes Risk Index (DRI), Metabolic Syndrome Score (MSS) and more—to analyze complex clinical dynamics. A detailed attempt of comparison study across logistic regression, decision trees, random forests, and deep neural networks enhanced by tuning model parameters [2] - indicates accuracy, precision. By integrating mathematical and statistical methodologies, this methodology advances early diabetes detection, supporting the revolutionary impact of AI-driven analytics in custom healthcare.

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A Data Science Framework for Enhanced Diabetes Prediction: Integrating Mathematical Modeling, Statistical Feature Engineering, and Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Ansh Soni,
  • Aneri Shah,
  • Krish Modi,
  • Nishant Doshi

摘要

This paper expresses an innovative method of data science architecture for enhanced diabetes prediction, embedding comprehensive mathematical framework, statistical feature engineering, and machine learning [1]. Utilizing the Pima Indian Diabetes dataset, we design and extract key features that drive prediction - Insulin-to-Glucose Ratio (IGR), Diabetes Risk Index (DRI), Metabolic Syndrome Score (MSS) and more—to analyze complex clinical dynamics. A detailed attempt of comparison study across logistic regression, decision trees, random forests, and deep neural networks enhanced by tuning model parameters [2] - indicates accuracy, precision. By integrating mathematical and statistical methodologies, this methodology advances early diabetes detection, supporting the revolutionary impact of AI-driven analytics in custom healthcare.