BrejBala/Heart-Disease-Prediction

❤️ Heart Disease Prediction (scikit-learn Random Forest)

A classic machine learning model that predicts the likelihood of heart disease from structured patient medical attributes (tabular data).
This repository contains a joblib-serialized scikit-learn model trained and evaluated in an end-to-end Jupyter Notebook workflow.

Model Details

• Developed by: brej-29
• Model type: RandomForestClassifier (scikit-learn)
• Task: Binary classification (tabular)
• Output labels:
  • 0 → No heart disease
  • 1 → Heart disease present
• Saved artifact: heart_disease_model.joblib
• Training notebook: HeartDiseasePredictionProject.ipynb
• Source code / project repo: https://github.com/brej-29/Logicmojo-AIML-Assignments-heart-disease-prediction-ml
• License: MIT

Intended Use

Direct Use

• Educational / portfolio demonstration of an end-to-end ML pipeline:
  • EDA → modeling → hyperparameter tuning → evaluation → persistence
• Research prototyping and experimentation with classical ML on healthcare-like tabular data

Out-of-Scope Use (Important)

• Not for clinical diagnosis
• Not a medical device
• Not validated for real-world patient care
• Do not use this model as the sole basis for medical decisions.

Training Data

The model was trained on a tabular dataset included in the project repository as heart-disease.csv.

• Rows: 303
• Columns: 14
  • Features: 13
  • Target: 1 (target)
• Target distribution:
  • 1: 165
  • 0: 138

Features (Input Schema)

The model expects 13 columns:

Feature	Description
age	Age in years
sex	Sex (commonly encoded as 1 = male, 0 = female)
cp	Chest pain type (categorical encoded as integers)
trestbps	Resting blood pressure
chol	Serum cholesterol
fbs	Fasting blood sugar (binary)
restecg	Resting ECG results (categorical encoded as integers)
thalach	Maximum heart rate achieved
exang	Exercise-induced angina (binary)
oldpeak	ST depression induced by exercise relative to rest
slope	Slope of peak exercise ST segment (categorical encoded as integers)
ca	Number of major vessels (categorical encoded as integers)
thal	Thalassemia category (categorical encoded as integers)

Training Procedure

Data Split

• train_test_split(test_size=0.2)
• Randomness controlled via np.random.seed(42) in the notebook

Candidate Models Explored

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Random Forest

Hyperparameter Tuning

• RandomizedSearchCV used to tune Random Forest
  • cv=5, n_iter=20
• Best Random Forest hyperparameters found in the notebook:
  • n_estimators=210
  • max_depth=3
  • min_samples_split=4
  • min_samples_leaf=19

Final Model

The saved model (heart_disease_model.joblib) corresponds to:

• RandomForestClassifier(n_estimators=210, max_depth=3, min_samples_split=4, min_samples_leaf=19)

Evaluation

Baseline Test Accuracy (single 80/20 split)

• KNN: ~0.689
• Logistic Regression: ~0.885
• Random Forest: ~0.836

Final Model Performance

• Loaded saved model test accuracy: 0.87

Cross-Validated Metrics (5-fold mean) — Final Random Forest

• Accuracy: 0.8479781421
• Precision: 0.8215873016
• Recall: 0.9272727273
• F1: 0.8705403543

Note: The notebook also visualizes confusion matrices and ROC curves for model comparison.

How to Use

1) Install dependencies

• pip install scikit-learn joblib pandas numpy huggingface_hub

2) Load the model from Hugging Face Hub

from huggingface_hub import hf_hub_download
import joblib
import pandas as pd

# Replace with your HF repo id, e.g. "brej-29/heart-disease-prediction-rf"
repo_id = "YOUR_USERNAME/YOUR_MODEL_REPO"

model_path = hf_hub_download(
    repo_id=repo_id,
    filename="heart_disease_model.joblib"
)

model = joblib.load(model_path)

# Example input (values are placeholders; use correctly-encoded values)
sample = pd.DataFrame([{
    "age": 57,
    "sex": 1,
    "cp": 0,
    "trestbps": 120,
    "chol": 354,
    "fbs": 0,
    "restecg": 1,
    "thalach": 163,
    "exang": 1,
    "oldpeak": 0.6,
    "slope": 2,
    "ca": 0,
    "thal": 2
}])

pred = model.predict(sample)[0]
proba = model.predict_proba(sample)[0, 1]  # probability of class "1"

print("Prediction:", int(pred))
print("P(heart disease):", float(proba))

Input Requirements

• Provide all 13 feature columns
• Ensure categorical features (cp, restecg, slope, ca, thal) follow the same integer encoding as used in training
• Numeric types should be valid numbers (int/float); no missing values

Bias, Risks, and Limitations

• Small dataset (303 rows) → results may not generalize to broader populations
• Encoding-dependent: categorical values must match training conventions
• No clinical validation: metrics are from offline evaluation only
• False negatives are possible (missed risk) — do not use for medical screening without rigorous validation

Environmental Impact

Training and evaluation were performed using classical ML methods on a small tabular dataset and are expected to have minimal compute and carbon impact (CPU-only, short runtime).

Technical Specifications

• Framework: scikit-learn
• Model format: joblib (heart_disease_model.joblib)
• Inference type: CPU-friendly tabular prediction

Model Card Authors

• BrejBala

Contact

For questions/feedback, please open an issue on the GitHub repository:
https://github.com/brej-29/Logicmojo-AIML-Assignments-heart-disease-prediction-ml