Year: 2025
Type: article
Abstract: : Cardiovascular disease (CVD) encompasses severe conditions that affect the heart and can be life-threatening. To improveearly diagnosis and treatment, researchers are leveraging advanced machine learning (ML) techniques to analyze electronic health data accurately. This study explores multiple ML approaches for predicting heart diseases using critical patient health factors. The implemented classification models include Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB). Before model training, data preprocessing and feature selection were performed toenhance prediction accuracy. The models wereevaluated using accuracy, precision,recall, and F1-score metrics, with the SVM model achieving the highest accuracy of 91.67%
Authors: Prof. Dilip Kale, Sejal Ghadge, Ketan Chavan, Amaan Ansari, Arin Pawar
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4409599323