Diabetes is a chronic condition that silently harms blood vessels and organs long before symptoms appear. By classifying diabetes risk from routine health data (age, BMI, blood glucose, HbA1c, etc.), we can warn earlier and prioritize proactive care. This leads to fewer complications, better quality of life, and lower healthcare costs.

By predicting the *diabetes* label from simple demographics, comorbidities and lab values, we surface high‑risk individuals for confirmatory testing and lifestyle/medical intervention. In practical terms, this enables targeted outreach, faster triage, and better resource planning in primary care.

In line with SDG 3 (Good Health & Well‑being) and SDG 10 (Reduced Inequalities), an early‑warning approach improves access to preventative care and reduces the burden of late‑stage complications.

Impact: catching diabetes early prevents avoidable hospitalizations and long‑term organ damage. Concretely, this means fewer acute admissions, fewer amputations, and better life expectancy.

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Hypothesis.

Diabetes likelihood increases with chronic hyperglycemia and metabolic stress. Concretely, higher HbA1c, higher fasting blood glucose, higher BMI, and older age are associated with a higher probability of diabetes; cardiovascular comorbidities add further signal.

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1. Project Goal

• Classify Diabetes Risk: Train a machine learning model that predicts whether a person is likely to have diabetes based on basic features available in routine care.
• Triage & Prioritization: Use model outputs to flag individuals for confirmatory testing and early intervention.
• Improve Outcomes: Support clinicians to act early (lifestyle coaching, diagnostic panels) and reduce preventable complications.
• Real-World Impact: Better-targeted care pathways and lower downstream costs via prevention-first population health. Concretely, this means:
  • For patients: earlier identification reduces the chance of serious complications such as kidney failure, cardiovascular events, and vision loss. People benefit from improved quality of life and fewer hospital stays.
  • For clinicians: risk-stratified decision support helps them prioritize limited time and resources, focusing on those most likely to benefit from additional testing or interventions.
  • For health systems: avoiding late-stage diagnoses reduces expensive acute treatments, emergency admissions, and long-term care needs. Resources can be shifted from crisis management to sustainable prevention.
  • For society: healthier working populations, reduced absenteeism, and lower insurance costs strengthen economic productivity and equity.

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In short, early machine-learning-based detection creates a triple win: patients receive timely support, clinicians get actionable guidance, and healthcare systems lower overall costs while improving outcomes.

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2. Who, what, when, where, why and how?

The predictions will be used by primary care teams and care coordinators to prioritize patients for confirmatory tests and coaching programs. Because the data comes from routine intake and basic labs, predictions can be generated on the spot in clinical workflows.

5W+H Framework

• Who: Primary care physicians, nurse practitioners, care coordinators.
• What: Use the model to flag likely diabetes for confirmatory testing and coaching.
• When: At intake and during routine follow-ups (point of care).
• Where: Primary care, community clinics, occupational health.
• Why: Manual risk rules miss interactions; ML scales and captures non-linear patterns.