Diabetes Prediction Using Support Vector Machine and Gradio

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Table of Contents

1. Project Overview
2. Dataset
3. Installation
4. Usage
5. Code Explanation
6. Gradio Interface
7. Conclusion

Project Overview

This project demonstrates how to predict diabetes using a Support Vector Machine (SVM) and Gradio for creating a user-friendly interface. The aim is to provide an accessible tool for predicting diabetes based on various health metrics.

Dataset

We use the PIMA Indian Diabetes Dataset, which includes health metrics such as pregnancies, glucose level, blood pressure, skin thickness, insulin level, BMI, diabetes pedigree function, and age. The dataset contains 768 entries labeled as diabetic or non-diabetic.

Installation

To run this project, you need to install the required dependencies. Create a requirements.txt file with the following content:

pandas
numpy
scikit-learn
gradio

You can install these dependencies using the following command:

pip install -r requirements.txt

Usage

Load the Dataset:

1. Place the diabetes.csv file in the same directory as your script.
2. Run the Script: Execute the app.py script to start the Gradio interface.

python app.py

Code Explanation

The project code is divided into several parts:

1. Importing Libraries and Loading the Dataset:

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn import svm
import gradio as gr

# Load the dataset
diabetes_dataset = pd.read_csv('diabetes.csv')
diabetes_dataset.head()

2.Data Preprocessing:

X = diabetes_dataset.drop(columns='Outcome', axis=1)
Y = diabetes_dataset['Outcome']
scaler = StandardScaler()
X = scaler.fit_transform(X)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.8, stratify=Y, random_state=1)

3.Model Training:

model = svm.SVC(kernel='linear')
model.fit(X_train, Y_train)

4.Defining the Prediction Function:

def predict_diabetes(Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age):
    input_data = pd.DataFrame([[Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age]],
                              columns=['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'])
    input_data = scaler.transform(input_data)
    prediction = model.predict(input_data)
    return 'You are Diabetic' if prediction[0] == 1 else 'Yay, you are Non-diabetic!'

5.Creating and Launching the Gradio Interface:

interface = gr.Interface(
    fn=predict_diabetes,
    inputs=[
        gr.Number(label="Pregnancies"),
        gr.Number(label="Glucose"),
        gr.Number(label="BloodPressure"),
        gr.Number(label="SkinThickness"),
        gr.Number(label="Insulin"),
        gr.Number(label="BMI"),
        gr.Number(label="DiabetesPedigreeFunction"),
        gr.Number(label="Age")
    ],
    outputs='text',
    title='Diabetes Prediction',
    description='Enter your details to predict if you are diabetic or not.'
)

interface.launch(share=True)

Gradio Interface

Gradio is used to create a web interface that allows users to input their health metrics and receive a prediction on whether they are diabetic or not. The interface simplifies the process of using the model and makes it accessible to users without a technical background.

Conclusion

This project demonstrates the application of machine learning in healthcare. Using an SVM model and Gradio, we can accurately predict diabetes and provide an easy-to-use interface for users. This approach can be extended to other health conditions, making healthcare more accessible and proactive.