Diabetes Classification

A PyTorch based implementation of a binary classification model for predicting the onset of diabetes. The model is trained on the Pima Indians Diabetes dataset, which consists of medical records for Pima Indians, and is designed to predict whether a patient will develop diabetes within five years.

Overview

The notebook guides you through the following steps:

1. Load Data
2. Define the PyTorch Model
3. Define Loss Function and Optimizers
4. Run a Training Loop
5. Evaluate the Model
6. Make Predictions

Dataset

Pima Indians Diabetes: This dataset contains patient medical record data for Pima Indians and indicates whether they had an onset of diabetes within five years. The problem is framed as a binary classification problem where the onset of diabetes is represented as 1 and no onset as 0.

The dataset has 9 columns:

• Input Variables (X):

  1. Number of times pregnant
  2. Plasma glucose concentration at 2 hours in an oral glucose tolerance test
  3. Diastolic blood pressure (mm Hg)
  4. Triceps skin fold thickness (mm)
  5. 2-hour serum insulin (μIU/ml)
  6. Body mass index (weight in kg/(height in m)^2)
  7. Diabetes pedigree function
  8. Age (years)

• Output Variable (y): 9. Class label (0 or 1)

Model Architecture

The model is a simple neural network with the following architecture:

• The input layer expects 8 features.
• The first hidden layer has 200 neurons with a ReLU activation function.
• The second hidden layer has 100 neurons with a ReLU activation function.
• The output layer has 1 neuron with a sigmoid activation function.

Getting Started

1. Clone this repository to your local machine:

git clone git@github.com:IsmaelMousa/diabetes-classification.git

2. Navigate to the diabetes-classification directory:

cd diabetes-classification

3. Setup virtual environment:

python3 -m venv .venv

4. Activate the virtual environment:

source .venv/bin/activate

5. Install the required dependencies:

pip install -r requirements.txt

6. Run the Jupyter Notebook:

jupyter-notebook

Results

The model's performance is evaluated by using the accuracy metric, and we got: 88.6%

Acknowledgments

The dataset used in this project is available on Kaggle: Pima Indians Diabetes.