🩺 Diabetes Prediction with CART Decision Tree

Binary classification model predicting diabetes onset using the Pima Indians Diabetes Dataset and a tuned CART (Classification and Regression Trees) decision tree.

Problem Statement

Given 8 diagnostic measurements, predict whether a patient has diabetes (Outcome = 1) or not (Outcome = 0). The dataset is the Pima Indians Diabetes Database containing 768 observations.

Dataset Features

Feature	Description
Pregnancies	Number of pregnancies
Glucose	Plasma glucose concentration
BloodPressure	Diastolic blood pressure (mm Hg)
SkinThickness	Triceps skin fold thickness (mm)
Insulin	2-Hour serum insulin (mu U/ml)
BMI	Body mass index
DiabetesPedigreeFunction	Diabetes pedigree function
Age	Age in years
Outcome	Target: 1 = diabetic, 0 = non-diabetic

Approach

1. EDA — Distribution analysis, target balance check, feature correlations
2. Missing Value Handling — Zeros treated as missing; imputed with class-conditional medians
3. Feature Engineering — BMI clinical categories (Underweight → Obesity 3), Insulin normality flag
4. Outlier Handling — IQR-based winsorization
5. Encoding — Label encoding for binary, rare encoding, one-hot encoding for multi-category
6. Model — CART Decision Tree with GridSearchCV (5-fold CV) over max_depth and min_samples_split

Results

Model	Accuracy	ROC-AUC
CART (default)	~72%	—
CART (tuned via GridSearchCV)	~75%	Computed via roc_auc_score

Why CART? Decision trees offer full interpretability — critical in healthcare applications where clinicians need to understand the reasoning behind predictions.

Engineered Features

Feature	Description
NewBMI	BMI bucketed into 6 clinical categories
New_Insulin	Insulin flagged as Normal (16–166) or Abnormal

Tech Stack

• Python 3.8+ — Core language
• Scikit-learn — DecisionTreeClassifier, GridSearchCV, metrics
• Pandas / NumPy — Data manipulation
• Seaborn / Matplotlib — Visualization
• pydotplus — Decision tree visualization

Project Structure

Diabetes_Prediction_ML_CART/
├── helpers/
│   ├── __init__.py
│   ├── data_prep.py          # Outlier handling, imputation, encoding utilities
│   └── eda.py                # EDA summary and visualization functions
├── Diabetes_prediction_CART.py  # Main ML pipeline
├── requirements.txt
└── README.md

Getting Started

git clone https://github.com/eboekenh/Diabetes_Prediction_ML_CART.git
cd Diabetes_Prediction_ML_CART
pip install -r requirements.txt

Download diabetes.csv from the Kaggle dataset and place it in the project root.

python Diabetes_prediction_CART.py

License

MIT