🩺 Intelligent Diabetes Risk Predictor

An end-to-end Machine Learning web app that predicts whether a patient is at risk of diabetes using clinical health data.

Final Year Project (FYP) — BS Computer Science
Virtual University, Lahore

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🎯 Project Overview

This system takes patient health information such as Glucose level, Blood Pressure, BMI, Age, etc. and predicts:

• ✅ Non-Diabetic (Low Risk)
• 🟡 Possibly Diabetic (Medium Risk)
• 🔴 Diabetic (High Risk)

Along with personalized health recommendations.

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📊 Dataset

• Source: Kaggle — Pima Indians Diabetes Dataset
• Rows: 768 patients
• Features: Glucose, Blood Pressure, BMI, Age, Pregnancies, etc.
• Target: Diabetic (1) or Non-Diabetic (0)
• Preprocessing: Zero values fixed, SMOTE applied for class balancing

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🤖 Models Trained & Compared

Model	Accuracy	Precision	Recall	F1 Score
Logistic Regression	70.1%	55.9%	65.0%	60.1%
Decision Tree	69.7%	54.9%	70.0%	61.5%
SVM ✅ Best	71.4%	56.5%	76.3%	64.9%

Winner: SVM — selected based on highest Recall and F1 Score.
In medical diagnosis, Recall is most important — missing a diabetic patient is dangerous.

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🖥️ App Features

• 🔍 Diabetes risk prediction (Low / Medium / High)
• 📋 Personalized health recommendations
• 📊 Dashboard — model performance comparison
• ℹ️ About page — project info

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⚙️ Tech Stack

• Language: Python
• ML: Scikit-learn, imbalanced-learn (SMOTE)
• App: Streamlit
• Data: Pandas, NumPy
• Visualization: Matplotlib, Seaborn

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🚀 How to Run

# Clone the repo
git clone https://github.com/HanzalaIftikhar/intelligent-diabetes-risk-predictor.git

# Install dependencies
pip install -r requirements.txt

# Run the app
streamlit run app.py

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👨‍💻 Developer

Hanzala Iftikhar
BS Computer Science — Virtual University, Lahore
📧 ranahanzala327@gmail.com
🔗 LinkedIn
🐙 GitHub

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⚠️ Disclaimer

This system is for educational purposes only and does not replace professional medical advice. Always consult a qualified doctor.