Direct impact:

The diabetes risk prediction model directly supports individuals by identifying those at higher risk of type 2 diabetes based on demographic, lifestyle, and health-related factors. By flagging potential high-risk cases early, healthcare providers can recommend preventive measures such as lifestyle changes or further medical testing before severe complications develop.

Secondary impact:

Beyond individual patients, the model contributes to reducing the long-term healthcare burden associated with untreated diabetes, including heart disease, kidney damage, and nerve problems. Earlier detection helps improve population health outcomes and lowers healthcare costs. It also builds trust in data-driven healthcare solutions, encouraging wider adoption of preventive medicine practices.

Key performance indicator (KPI):

The model aims for at least 80% recall on diabetic cases, ensuring that the majority of at-risk individuals are successfully identified and not missed during screening. This balance prioritizes patient safety while maintaining overall model reliability.

SDG link:

This project aligns with SDG 3 – Good Health and Well-being by promoting early detection and prevention of non-communicable diseases, and SDG 10 – Reduced Inequalities by making predictive healthcare tools more accessible and equitable across diverse populations.