Diabetes Prediction Using Data Science & Machine Learning (MATLAB)

This MATLAB-based Project implements a diabetes prediction system using machine learning and an intuitive graphical user interface (GUI). Designed as part of a college assignment, it empowers users to quickly assess diabetes risk based on simple health metrics.

Objective

To build an accessible, early-stage diabetes risk predictor using the Pima Indian Diabetes Dataset, making health screening more scalable and user-friendly — especially for remote or underserved areas.

Tools & Technologies

• MATLAB (for scripting and GUI)
• Statistics and Machine Learning Toolbox
• Random Forest (Bagged Trees Classifier)
• GUI designed using uicontrol and uipanel

Dataset

The Pima Indian Diabetes dataset (from Kaggle) includes 768 patient records with 9 key medical attributes:

• Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI
• Diabetes Pedigree Function, Age
• Outcome (0 = Non-diabetic, 1 = Diabetic)

Methodology

Data Preprocessing

• Missing values (e.g., Insulin, Skin Thickness) replaced with averages.
• Z-score normalization applied to input features.

Model

• Random Forest (Bagged Ensemble) classifier chosen for its robustness with tabular data.
• Trained on 70% of the dataset using cvpartition in MATLAB.

Feature Engineering

• BMI is auto-computed from user-entered weight and height.
• GUI dynamically adjusts input fields (e.g., pregnancies only shown for female users).

GUI Interface

Three interactive pages:

1. User Info: Name, Age, Gender
2. Health Data: Glucose, Blood Pressure, Weight, Height, Insulin, Pregnancies
3. Family History (optional)

Color-coded messages and a refresh button enhance usability.

Home Screen

Prediction Result

Input Form

Implementation Highlights

• Core script: DiabetesPredictorUI.m
• Full input validation, auto-calculated BMI, and clear result display.
• Result shown as High Risk / Low Risk, with recommendations if needed.

Results

• Accurately flags diabetes likelihood from user inputs.
• Enhances medical accessibility through digital tools.

How to Run

1. Clone/download this repository.
2. Open MATLAB and navigate to the project folder.
3. Run DiabetesPredictorUI.m.
4. Input sample data via GUI to get predictions.

References

• Pima Diabetes Dataset – Kaggle
• MathWorks: fitcensemble
• MathWorks: uicontrol

Contact

For questions or collaboration:

Email: rishabhrsingh19@gmail.com
Linkedin: https://www.linkedin.com/rishabh-ranjan-singh