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:

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