Detecting cardiovascular disease by comparing recurrent neural network (RNN) and random forest (RFC) for improvising accuracy rate
Abstract
Research for Cardiovascular disease by analyzing parameters for improvising accuracy rate through Recurrent Neural Network Algorithm with Random forest algorithm is in this work. Recurrent Neural Networks and Random Forest, two deep learning techniques, were used to extract the dataset from Kaggle. To assess the unique recurrent neural networks and Random forest classifiers carefully considered in the Deep Learning repository in this study, 1026 data are collected for each group. Gender, age, test-taking habits, cholesterol, smoking, obesity, unhealthy diet, family history, and significance (p
Fields of Study & Subjects
Artificial Intelligence in Healthcare
ECG Monitoring and Analysis
Data Mining and Machine Learning Applications
Publication details
- Journal / source
- AIP conference proceedings
- Volume
- 3393
- Pages
- 020022–020022
- Publication date
- 1 January 2026
- Language
- EN