Predicting Heart Disease with Advanced Machine Learning Techniques

Salma El Hamdi; Khaoula Refaat; Wafae Abbaoui; Imane Lasri; Anouar Riadsolh; Soumia Ziti

Article Details

Predicting Heart Disease with Advanced Machine Learning Techniques

Authors:

Salma El Hamdi | Khaoula Refaat | Wafae Abbaoui | Imane Lasri | Anouar Riadsolh | Soumia Ziti

Publisher:

The National Center for Scientific and Technical Research, Morocco

Journal:

Journal of Innovation and Digital Health Vol.1 Issue 2, pp.43-52

Document Type:

Article

Publication Date:

10/2024

Document Language:

English

Document ID (Org No.):

2001847351

pISSN:

3009-5417

Keywords:

Abstract:

 Heart disease remains a leading cause of mortality worldwide, highlighting the need for effective predictive tools. However, many studies struggle to report their results effectively. Objective: This study aims to improve the accuracy and efficiency of heart disease diagnosis by employing machine learning and deep learning techniques. It also underscores the importance of raising awareness and implementing preventive measures to reduce heart disease mortality. We utilized the Random Forest algorithm, a supervised classification technique that builds multiple decision trees, to predict heart disease. This algorithm was selected for its capacity to minimize overfitting, handle both numerical and categorical data, manage outliers, and provide insights into feature importance. Hyperparameter tuning was performed using random search to enhance model performance. The effectiveness of the model was assessed with metrics such as precision, F1-score, AUC-ROC, and recall, demonstrating significant improvements in prediction accuracy and reliability.  The Random Forest model achieved an accuracy of 96.7% on the test set. Performance metrics, including precision, recall, and F1-score, were approximately 0.97. The model’s AUC-ROC was 0.92, indicating excellent classification ability.  This study presents a robust framework for heart disease prediction through machine learning, delivering highly accurate results. The Random Forest model’s ability to process diverse data types and its feature importance insights make it well-suited for medical data analysis. Future research should explore additional algorithms and larger datasets to further enhance predictive capabilities.

Article Details

Predicting Heart Disease with Advanced Machine Learning Techniques

Authors:

Salma El Hamdi | Khaoula Refaat | Wafae Abbaoui | Imane Lasri | Anouar Riadsolh | Soumia Ziti

Publisher:

The National Center for Scientific and Technical Research, Morocco

Journal:

Journal of Innovation and Digital Health Vol.1 Issue 2, pp.43-52

Document Type:

Article

Publication Date:

10/2024

Document Language:

English

Document ID (Org No.):

2001847351

pISSN:

3009-5417

Heart Disease

Deep learning

Cardiovascular Health

Random Forest Classifier

Issues

Article Details

Predicting Heart Disease with Advanced Machine Learning Techniques

Authors: Salma El Hamdi | Khaoula Refaat | Wafae Abbaoui | Imane Lasri | Anouar Riadsolh | Soumia Ziti

Publisher: The National Center for Scientific and Technical Research, Morocco

Journal: Journal of Innovation and Digital Health Vol.1 Issue 2, pp.43-52

Document Type: Article

Publication Date: 10/2024

Document Language: English

Document ID (Org No.): 2001847351

pISSN: 3009-5417

Heart Disease

Deep learning

Cardiovascular Health

Random Forest Classifier

Issues

Authors:

Salma El Hamdi | Khaoula Refaat | Wafae Abbaoui | Imane Lasri | Anouar Riadsolh | Soumia Ziti

Publisher:

The National Center for Scientific and Technical Research, Morocco

Journal:

Journal of Innovation and Digital Health Vol.1 Issue 2, pp.43-52

Document Type:

Article

Publication Date:

10/2024

Document Language:

English

Document ID (Org No.):

2001847351

pISSN:

3009-5417