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A Machine Learning Tool for Early Intervention

Heart disease is a leading cause of death worldwide, and accurate prediction of heart disease remains a significant challenge.

This project aims to develop a machine learning model capable of predicting heart disease using a comprehensive dataset of key indicators.

• A dataset of over 400,000 adult profiles, capturing the diverse health status of individuals across various demographics and risk factors
• A range of machine learning models, including Decision Trees, Random Forests, Gradient Boosting, and more
• Hyperparameter tuning using BayesSearchCV and RandomizedSearchCV
• Model evaluation using classification reports and accuracy scores
• Prediction on new, unseen patient data from a random sample from the test set

• Dataset URL:💖 Indicators of Heart Disease
• License: CC0-1.0
• Number of samples: 400,000
• Number of factors: 40

Requirements

• Python 3.x
• Xgboost
• Keras
• Scikit-learn
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Plotly

Data Preprocessing 🔀

• Data Scaling: Appling PCA to the training features, normalize categorical labels, and shuffle the dataset to increase randomness and reduce bias. 🔀

Models 🤖

The following models are implemented and compared:

1. DecisionTreeClassifier
2. RandomForestClassifier
3. ExtraTreesClassifier
4. GradientBoostingClassifier
5. HistGradientBoostingClassifier
6. XGBClassifier
7. LGBMClassifier
8. CatBoostClassifier
9. SVC
10. LogisticRegression
11. MLPClassifier
12. AdaBoostClassifier
13. GaussianNB

Model Performance 📊

The model achieves a test accuracy of 94.66% using the MLPClassifier model, which is a great result considering the complexity of the dataset! 🎉I have also identified the best hyperparameters for the RandomForestClassifier and XGBClassifier models using BayesSearchCV and RandomizedSearchCV.

• Training accuracy: 0.9996
• Validation accuracy: 0.9420
• Test accuracy: 0.9600

Hyperparameter Tuning 🔧

• GridSearchCV
• RandomizedSearchCV

• Kaggle dataset: 💖 Indicators of Heart Disease (2022 UPDATE)
• Scikit-learn and Xgboost libraries for model training
• Matplotlib and Seaborn libraries for data visualization

This project is licensed under AGPL-3.0 License and is for personal use only and should not be used for commercial purposes.The pre-trained model and may not always produce accurate results.

This project demonstrates the potential of machine learning for heart disease prediction.The model achieves high accuracy and can be used as a starting point for further research and development in this field.

I hope you found this project informative and engaging! 😊
If you're interested in collaborating and contributing to the project, please let me know! I'd love to hear from you.

• Follow me on GitHub
• Follow me on Hugging Face

To get started with this project, you'll need to:

• Install the required libraries, including pandas, numpy, scikit-learn, xgboost, catboost, lightgbmpip install pandas numpy scikit-learn tensorflow xgboost lightgbm catboost 📦
• Download the dataset from Kaggle 📈
• Run the code to train and evaluate the model 🤖

Enjoy working with the content! 😊