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🩺 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

🎯 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.

📊 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

🤖 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.

🖥️ App Features

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

⚙️ Tech Stack

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

🚀 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

👨‍💻 Developer

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

⚠️ Disclaimer

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