Predicting Diabetes Mellitus Using Machine Learning Techniques 🩺🤖

Diabetes Prediction and Analysis Report

Overview

This repository encompasses code and resources dedicated to predicting diabetes outcomes using advanced machine learning techniques. The comprehensive report details data preprocessing, exploratory data analysis (EDA), and the implementation of Decision Trees, Support Vector Machine (SVM), Random Forest, and a Neural Network. The primary objective is to gain insights into the key factors influencing diabetes and construct accurate prediction models.

Introduction

Background: Diabetes is a widespread health concern, demanding precise prediction for effective management and prevention. This report engages with a dataset, employing machine learning models to forecast diabetes outcomes.

Problem Statement: The timely diagnosis and efficient management of diabetes are critical. Machine learning models serve as invaluable tools for early intervention and treatment.

Motivation: The motivation behind this project is to craft precise prediction models for diabetes outcomes, providing healthcare professionals with a deeper understanding of key influencing factors.

Literature Review

Numerous studies have explored machine learning applications in diabetes prediction, utilizing algorithms such as decision trees, support vector machines, random forests, and neural networks. This report emphasizes the significance of feature engineering and data preprocessing for enhancing model performance.

System Overview

Key Components:

1. Data Preprocessing: Includes handling missing values, feature selection, and standardization.
2. Exploratory Data Analysis (EDA): Visualizes and comprehends the dataset's characteristics.
3. Machine Learning Models: Implements Decision Trees, SVM, Random Forest, and Neural Network.
4. Model Evaluation: Assesses performance through accuracy, confusion matrices, and ROC curves.

Project Workflow

1. Import Libraries: Utilizes essential libraries such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, TensorFlow, Keras, and Pydotplus.

2. Dataset Overview: Analyses a comprehensive dataset containing health-related features and the target variable "Outcome."

3. Import Dataset: Loads the dataset for subsequent analysis.

4. Data Preprocessing:

   • Handling Missing Values: Replaces missing values with appropriate measures.
   • Feature Selection: Identifies key features for model development.
   • Data Standardization: Standardizes data using StandardScaler.

5. EDA:

   • Data Visualization: Utilizes histograms, pair plots, and correlation heatmaps to extract insights.
   • Feature Analysis: Explores feature impact, with a particular focus on Glucose's strong correlation with outcomes.

6. Machine Learning Models:

   • Decision Trees: Implements and visualizes a decision tree classification model.
   • SVM: Develops SVM models with RBF and Linear kernels, evaluating accuracy and confusion matrices.
   • Random Forest: Implements and visualizes a Random Forest classification model.
   • Neural Network: Develops a simple neural network model, visualizing training and validation metrics.

Software and Hardware Tools

Software Tools:

• Python: Utilized for data analysis, model development, and visualization.
• Jupyter Notebook: Employed for code development and documentation.

Hardware Tools:

• CPU: Standard CPUs with sufficient RAM for analysis.
• GPU (Optional): Accelerates neural network training.

Conclusion

Results:

• Decision Trees: Achieved an accuracy of 0.73.
• SVM (RBF kernel): Achieved an accuracy of 0.80.
• SVM (Linear kernel): Achieved an accuracy of 0.80.
• Random Forest: Achieved an accuracy of 0.78.
• Neural Network: Achieved an accuracy of 0.76.

Recommendations:

• Glucose emerges as a significant predictor.
• Further feature engineering and tuning could enhance model performance.
• Regular monitoring and early intervention play a vital role in effective diabetes management.

References

1. Diabetes Dataset
2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
3. Pattern Recognition and Machine Learning
4. Scikit-Learn Documentation - Decision Trees
5. Support Vector Machine - Introduction to Machine Learning Algorithms
6. Scikit-Learn Cheat Sheet
7. CS229 Lecture notes Support Vector Machines