In this project, we build a Rule-Based Diabetes Risk Checker using Python Flask. The application allows users to input basic health indicators such as glucose level, BMI, and age. The system then evaluates these values using predefined rules and instantly provides a risk assessment such as Low, Medium, or High risk of diabetes.
It also serves as a great beginner-friendly project for students learning Python, Flask, and AI fundamentals.
Why Use a Rule-Based System?
Rule-based systems use if-else logic to simulate expert knowledge. Unlike machine learning models, they do not require training data and can be understood and modified easily. In early healthcare AI systems, rule-based logic was widely used because the criteria for certain conditions are well established.
For diabetes, medical guidelines provide clear cutoff values:
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Normal: Fasting glucose < 100 mg/dL
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Prediabetes: 100–125 mg/dL
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Diabetes likely: ≥ 126 mg/dL
This makes diabetes assessment a great example for a rule-based AI project.
*** This project is for learning purposes only. It is not a diagnostic tool and should not replace professional medical advice.
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