Diabetes Prediction Using Machine Learning

Project Overview

Diabetes is a widespread and serious chronic condition affecting millions globally. Early and accurate prediction is essential for effective disease management and prevention. This project leverages machine learning (ML) techniques to enhance diabetes prediction accuracy, offering a more efficient alternative to traditional diagnostic methods.

Objective

Develop an advanced Diabetes Prediction model using state-of-the-art data science methodologies and ML algorithms. The project aims to provide accurate predictions by:

• Leveraging comprehensive datasets
• Employing advanced data preprocessing, feature engineering, and modeling techniques
• Evaluating and optimizing model performance

Problem Statement

Traditional diagnostic methods for diabetes, while effective, can be enhanced through ML techniques. This project aims to:

• Create a robust Diabetes Prediction model using the Pima Indians Diabetes Database.
• Analyze various health indicators (glucose levels, blood pressure, BMI, age) to predict diabetes risk.
• Uncover subtle correlations within the health data to improve prediction accuracy.

Procedure

1. Understanding the Business Problem

• Objectives: Develop a model to assist in early diagnosis and treatment planning.
• Scope: Data collection, preprocessing, exploratory data analysis (EDA), model training, evaluation, and deployment.
• Requirements: Pima Indians Diabetes Database, Python libraries (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Streamlit), computational resources.

2. Preparing the Data

• Data Collection: Sourced from the Pima Indians Diabetes Database.
• Data Cleaning: Handle missing values, remove noise, and address outliers.
• Data Integration & Transformation: Prepare data for analysis, transforming categorical variables into numerical formats if needed.

3. Exploratory Data Analysis (EDA)

• Descriptive Statistics: Basic statistics to understand data distribution.
• Data Visualization: Use bar charts, heat maps, histograms, and more to identify patterns and relationships.
• Pattern Identification: Inform feature selection and engineering.

4. Modelling the Data

• Selecting Algorithms: Evaluate algorithms like Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines. Random Forest was chosen for its robustness.
• Training Models: Split data into training (80%) and testing (20%) sets. Train the Random Forest model on the training data.
• Validation: Use cross-validation to ensure robustness and prevent overfitting.

5. Evaluating the Model

• Performance Metrics: Accuracy, precision, recall, F1 score, and confusion matrix.
• Cross-Validation: Assess model performance across different data subsets.
• Comparison & Interpretability: Compare models and ensure predictions are interpretable.

6. Deploying the Model

• Model Integration: Implement the model in a Streamlit web application for real-time predictions.
• Monitoring & Updating: Track model performance and update with new data.
• Documentation: Comprehensive documentation for future reference and compliance.

Results

Random Forest Model

• Accuracy: 80.2%
• The Random Forest model demonstrated high accuracy, effectively balancing variance and bias, and providing robust performance for diabetes prediction.

Summary

The Random Forest model proved to be a reliable choice for predicting diabetes with high accuracy. Its ability to capture various data patterns makes it a strong candidate for this prediction task.

References

• Books: "Introduction to Machine Learning with Python" by Andreas C. Müller and Sarah Guido.
• Websites and Blogs: Kaggle – Pima Indians Diabetes Database.

Live App

Check out the live model in action on Streamlit