Enhancing Cardiovascular Diseases Classification using CNN Algorithm

Dublin Core

Title

Enhancing Cardiovascular Diseases Classification using CNN Algorithm

Subject

Cardiovascular Disease Detection, Machine Learning Algorithms, CNN, SVM, Medical Diagnosis

Description

This study focuses on using machine learning algorithms to detect cardiovascular diseases, addressing the critical need for accurate and timely diagnosis of these conditions, which are significant contributors to global morbidity and mortality. The research aims to evaluate the performance of various machine learning algorithms such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting in categorizing patients into 'yes' or 'no' groups for cardiovascular diseases based on a thorough dataset. The methodology includes data preprocessing, feature selection, and model training and assessment. The results indicate that CNN and SVM demonstrate strong and balanced performance, whereas the Decision Tree shows high sensitivity but potential overfitting. These outcomes offer valuable insights for algorithm selection and model improvement in the detection of cardiovascular diseases, setting the groundwork for further research to enhance diagnostic accuracy, clinical relevance, and healthcare outcomes

Creator

Romana H,Juwita Sampe R

Source

https://ijicom.respati.ac.id/index.php/ijicom/article/view/60/49

Date

December 2023

Contributor

Fajar bagus W

Format

PDF

Language

English

Type

Text

Files

Collection

Citation

Romana H,Juwita Sampe R, “Enhancing Cardiovascular Diseases Classification using CNN Algorithm,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8389.