Heart Disease Prediction Model (XGBoost)

Overview

This repository contains a trained XGBoost classification model for predicting the likelihood of heart disease based on patient clinical information.

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Heart Disease Prediction Model (XGBoost)

Overview

This repository contains a trained XGBoost classification model for predicting the likelihood of heart disease based on patient clinical information.

The model was trained using a heart disease dataset containing demographic, clinical, and diagnostic features commonly used in cardiovascular risk assessment.

Model Information

Algorithm: XGBoost Classifier
Task: Binary Classification
Target Variable: Heart Disease Presence
- 0 → No Heart Disease
- 1 → Heart Disease Detected

Input Features

The model expects the following 13 features:

Feature	Description
age	Age of the patient
sex	Gender (0 = Female, 1 = Male)
cp	Chest Pain Type
trestbps	Resting Blood Pressure
chol	Serum Cholesterol
fbs	Fasting Blood Sugar (>120 mg/dl)
restecg	Resting Electrocardiographic Results
thalach	Maximum Heart Rate Achieved
exang	Exercise-Induced Angina
oldpeak	ST Depression Induced by Exercise
slope	Slope of Peak Exercise ST Segment
ca	Number of Major Vessels Colored by Fluoroscopy
thal	Thalassemia Status

Model File

heart_disease_xgb.pkl

This file contains the trained XGBoost model serialized using Joblib.

Loading the Model

import joblib

model = joblib.load("heart_disease_xgb.pkl")

Example Prediction

import pandas as pd
import joblib

model = joblib.load("heart_disease_xgb.pkl")

sample = pd.DataFrame([{
    "age": 52,
    "sex": 0,
    "cp": 2,
    "trestbps": 136,
    "chol": 196,
    "fbs": 0,
    "restecg": 0,
    "thalach": 169,
    "exang": 0,
    "oldpeak": 0.1,
    "slope": 1,
    "ca": 0,
    "thal": 2
}])

prediction = model.predict(sample)
probability = model.predict_proba(sample)

print("Prediction:", prediction[0])
print("Probability:", probability)

Dependencies

xgboost
pandas
numpy
scikit-learn
joblib

Install requirements:

pip install xgboost pandas numpy scikit-learn joblib

Intended Use

This model is intended for:

Educational projects
Machine Learning demonstrations
Healthcare analytics research
Streamlit and Hugging Face deployments

Disclaimer

This model is designed for educational and research purposes only. Predictions should not be considered medical advice or used for clinical decision-making without professional medical evaluation.

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