Diabetes Health Indicators - Machine Learning Project

COMP90049, Introduction to Machine Learning, Fall 2025
Group: Phoenix (G06)
School of Computing and Information Systems, The University of Melbourne

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Project Overview

This project analyzes the Diabetes Health Indicators Dataset using various machine learning classification algorithms to predict diabetes status. The target variable has three classes: No diabetes, Prediabetes, and Diabetes.

We implement and compare four different machine learning models with comprehensive preprocessing, feature selection, and hyperparameter tuning to achieve optimal classification performance.

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Research Paper

You can read the full research paper here:

View PDF

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Code

The complete implementation and analysis can be found in the Jupyter notebook:

View Notebook

Note: GitHub renders Jupyter notebooks natively, so you can view the code, visualizations, and results directly in the browser without downloading the file.

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Dataset

The dataset used is the Diabetes Health Indicators Dataset from Kaggle:

• Source: Kaggle Dataset
• File: data/dataset.csv
• Description: Contains health indicators and demographic information used to predict diabetes status

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Models Implemented

The project implements and compares the following machine learning models:

• K-Nearest Neighbors (KNN)
• Logistic Regression
• Decision Tree
• Multilayer Perceptron (MLP) Neural Network

Methodology

All models utilize:

• Feature Selection: Mutual information-based feature selection
• Class Imbalance Handling: SMOTE (Synthetic Minority Oversampling Technique) and undersampling
• Hyperparameter Tuning: GridSearchCV with cross-validation
• Evaluation Metrics: Accuracy, Precision, Recall, F1-score, and ROC-AUC

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Getting Started

Prerequisites

• Python 3.7+
• Jupyter Notebook or JupyterLab
• pip (Python package manager)

Installation

1. Clone or download this repository:

   git clone <repository-url>
   cd MLproject

2. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Ensure the dataset file data/dataset.csv exists in the project directory
2. Open MachineLearning_DiabitesPredictionsCode.ipynb in Jupyter Notebook
3. Run all cells sequentially to reproduce the analysis

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Requirements

All required Python packages are listed in requirements.txt. Key dependencies include:

• pandas - Data manipulation and analysis
• numpy - Numerical computing
• scikit-learn - Machine learning algorithms and utilities
• imbalanced-learn - Handling class imbalance
• matplotlib & seaborn - Data visualization
• jupyter - Notebook environment

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Group Members

• Xinyang Sun – xinyang.sun.3@student.unimelb.edu.au
• Nina Lock – ninalock@student.unimelb.edu.au
• Vadym Musiienko – vmusiienko@student.unimelb.edu.au

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Acknowledgments

• Dataset provided by Kaggle
• This project was completed for COMP90049 Introduction to Machine Learning at The University of Melbourne, Fall 2025