Diabetes Prediction Using Machine Learning Based Web App

Diabetes Prediction

Overview

This project is a professional-level web app built to predict the risk of diabetes in patients using clinical details like glucose level, BMI, age, and insulin levels.It uses a machine learning model trained on the PIMA Indian Diabetes dataset and is developed with Flask, a lightweight Python framework.The goal is to support early diagnosis by letting healthcare professionals or users enter patient data and get quick feedback on the risk of diabetes.The process includes collecting and cleaning the data, building and testing various machine learning models, and selecting the most accurate one. The final model is then saved and linked to a simple web interface for easy use.

Project Details

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Installation

1. Clone the repository and unzip the project files.
2. Navigate to the Flask directory: cd flask
3. Create and activate a virtual environment using Python 3.
4. Install required packages: pip install -r requirements.txt
5. Launch the application: python app.py
6. Open your browser and go to: http://127.0.0.1:5000/

Available Features

• Upload patient health metrics like Glucose, BMI, Age, etc.
• Predict if the patient is likely to have diabetes
• Real-time results using a pre-trained machine learning model
• Simple and elegant UI using HTML and CSS
• Backend model built with Python and scikit-learn
• Responsive form handling in Flask

Technologies Used

• Frontend: HTML, CSS
• Backend: Python, Flask
• ML Libraries: Pandas, scikit-learn, joblib
• Dataset: diabetes.csv (PIMA Indians Diabetes Database)

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