This repository contains a Flask web application designed to predict diabetes using a K-Nearest Neighbors (KNN) model. The prediction is based on user-inputted medical attributes, such as Glucose level, Blood Pressure, and BMI, among others. The model was trained on a dataset containing several medical attributes of individuals and whether or not they had diabetes.

• analysis.ipynb - Jupyter notebook containing the analysis of different machine learning algorithms on the dataset and their performance.
• app.py - The Flask application.
• model.py - Script used to train the model and generate knn_model.pkl and scaler.pkl.
• /templates/index.html - Contains HTML files for the web interface.
• /static/style.css - Contains CSS files for styling the web interface.
• knn_model.pkl - The serialized KNN model used for making predictions.
• scaler.pkl - The serialized scaler used to normalize input features.

The analysis conducted in analysis.ipynb tested various machine learning models on the dataset to identify which model provides the most accurate predictions for diabetes. The results of the analysis are as follows:

• K-Nearest Neighbors (KNN): Achieved the highest accuracy of 81.1%.
• Support Vector Machine (SVM): Achieved an accuracy of 79.8%.
• Logistic Regression: Achieved an accuracy of 80.5%.

Based on this analysis, the KNN model was chosen for the web application due to its superior performance.

To run Diabetes-Prediction on your local machine, you need Python 3.6+ installed. Follow these steps:

1. Clone this repository to your local machine.

   git clone https://github.com/sherwinvishesh/Diabetes-Prediction.git

2. Navigate to the project directory.

   cd Diabetes-Prediction

3. Install the required Python packages.

   pip install Flask scikit-learn numpy pandas

   If you are getting any errors while installing the above Then you have to create a virtual environment and run this program, here are the steps: Create a Virtual Environment:

   python3 -m venv path/to/venv

   Activate the Virtual Environment: mac or linux

   source path/to/venv/bin/activate

   For Windows:

   path\to\venv\Scripts\activate

4. Run the model.py to generate knn_model.pkl and scaler.pkl

   python3 model.py

5. Run the Flask application.

   python3 app.py

After running the application and accessing it at http://127.0.0.1:5000/, follow these steps to receive a diabetes prediction:

1. Enter Medical Information: Fill in the form with the medical attributes required by the model. The required inputs include:

   • Pregnancies: Number of times pregnant
   • Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test
   • Blood Pressure: Diastolic blood pressure (mm Hg)
   • Skin Thickness: Triceps skin fold thickness (mm)
   • Insulin: 2-Hour serum insulin (mu U/ml)
   • BMI: Body mass index (weight in kg/(height in m)^2)
   • Diabetes Pedigree Function: A function that scores the likelihood of diabetes based on family history
   • Age: Age (years)

2. Submit for Prediction: After filling in the form with the necessary medical information, click the "Predict" button to submit the data for prediction.

3. View Prediction Result: The prediction result will be displayed on the page, indicating whether the inputted attributes suggest a diagnosis of diabetes ("Yes" for diabetic, "No" for not diabetic).

• Suppose you want to know the diabetes prediction for an individual with the following attributes:

  • Pregnancies: 2
  • Glucose: 138
  • Blood Pressure: 62
  • Skin Thickness: 35
  • Insulin: 0
  • BMI: 33.6
  • Diabetes Pedigree Function: 0.627
  • Age: 47

  You would enter these values into the form and click "Predict" to see if the model predicts this individual as diabetic or not.

Contributions to enhance this project are welcomed. Please feel free to fork the repository, make changes, and submit pull requests.

If you encounter any issues or have any questions, please submit an issue on the GitHub issue tracker or feel free to contact me.

This project is open source and available under the Apache-2.0 license.

• Thanks to everyone who visits and uses this page. Your interest and feedback are what keep us motivated.
• Special thanks to all the contributors who help maintain and improve this project. Your dedication and hard work are greatly appreciated.
• Special acknowledgment to Kavya11-2 for her project Diabetic_Prediction. It served as a significant inspiration for this project, demonstrating the powerful impact of Machine Learning.

This tool is intended for informational purposes only and is not a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition.

Feel free to reach out and connect with me on LinkedIn or Instagram.

Made with ❤️ by Sherwin