Enhancing Early Detection of Cardiovascular Disease through Feature Optimization Methods
subject: Cardiovascular detection (CVD); Heart disease; Machine Learning; Optimization
description: cardiovascular diseases are the most common reason for mortality around the world. Early detection of the ailment can help to reduce the mortality rate considerably. The ever-growing technologies like machine learning algorithms and deep learning models can be used for this purpose. The AI models thus developed can be used for health sector for assisting doctors in assessing the stage of the disease and detection and tracking of the clots in the cardio blood vessels. The proposed work uses two benchmark datasets for analysing the performance of various machine learning algorithms including KNN, Nae Bayes, Decision Tree and Random Forest. The performance was compares based on the AUC %. The method feature reduction were used here to reduce the computational complexity of the model. The results show that Random Forest Algorithm gave the best result when compared to other algorithms in case of UCI dataset and MLP classifier gave best results for Kaggle dataset. 2024 IEEE.
creator: Didwania S.; Amrutha K.; Resmi K.R.; Vineetha K.R.
source: 2024 IEEE 3rd World Conference on Applied Intelligence and Computing, AIC 2024, pp. 119-122.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835038459-8
format: Online
language: English
type: Conference paper
coverage: Didwania S., Christ University, Department of Computer Science, Bengaluru, India; Amrutha K., Christ University, Department of Computer Science, Bengaluru, India; Resmi K.R., Christ University, Department of Computer Science, Bengaluru, India; Vineetha K.R., Christ University, Department of Computer Science, Bengaluru, India