Comparative Analysis Of Machine Learning Models Of Diabetes Prediction

• Author(s):

  AKASH J

• Keywords:

  Diabetes Prediction, Machine Learning, HbA1c, Glucose Classification, React Dashboard, Clinical Decision Support, Health Analytics, BMI, AUC-ROC, Web-based Health Application

• Abstract:

  The Diabetes Mellitus Is One Of The Fastest-growing Chronic Health Conditions Globally, Affecting Hundreds Of Millions Of Individuals And Imposing Significant Burdens On Healthcare Systems Worldwide. Early Identification Of At-risk Individuals Is Essential For Timely Intervention, Lifestyle Modification, And Prevention Of Severe Complications Such As Nephropathy, Neuropathy, Cardiovascular Disease, And Vision Impairment. This Paper Presents DiaPredict, An Intelligent, Web-based Clinical Dashboard Built Using React, Tailwind CSS, And Framer Motion, Designed To Deliver Real-time Diabetes Risk Prediction Through A Multi-parameter AI Model. The System Accepts Fourteen Clinically Validated Input Parameters Including Age, Gender, BMI, Fasting Or Post-meal Glucose Level, HbA1c Percentage, Blood Pressure, Insulin Level, Skin Thickness, Diabetes Pedigree Function, Physical Activity, Smoking, Alcohol Consumption, And Pregnancy History.

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Other Details

• Paper id:

  IJSARTV12I6105696

• Published in:

  Volume: 12 Issue: 6 June 2026

• Publication Date:

  2026-06-18

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