Machine Learning Techniques and Insights for Cardiovascular or Heart Disease Prediction
Heart disease has been one of the major health concerns over the decades regardless of age, weight, or gender. Here in the article, we try to highlight the importance of early detection and the prevention of life-threatening heart-related diseases, including heart attacks and strokes. For this review paper, we include raw data from clinics and also include the famous Framingham Heart Study dataset, which is a popular cardiovascular dataset, including patient records that emphasize important human risk factors including age, blood pressure, cholesterol, smoking status, and family history. Precision, recall, accuracy, F1-score, and ROC-AUC metrics are such techniques and algorithms that are used to evaluate the implementation and performance of advanced machine learning approaches, including Gradient Boosting Machines (GBM), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Convolutional Neural Network (CNN) and many more. Here, we
Authors
- Mujiba Shaima (ORCID: https://orcid.org/0009-0004-9129-0716)
- Estak Ahmed
- Sharmin Sultana Akhi (ORCID: https://orcid.org/0000-0002-7904-9004)
- Mazharul Islam Tusher
- Rayhan Hassan Mahin
Institutions
- Monroe University (US)
- University of Louisiana at Monroe (US)
Publication Details
- Journal
- Academic International Journal of Engineering Science
- Published
- 2025-06-27
- DOI
- https://doi.org/10.59675/e313
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.94