Deep Neural Network based Heart Disease Prediction

DOI

10.2991/978-94-6463-512-6_70How to use a DOI?

Keywords

Heart Disease; Deep Neural Network; Machine learning; Feature selection; Imbalance learning

Abstract

Since the 21st century, cardiovascular disease (CVD), which has a high rate of morbidity and mortality, has become one of the most prevalent and fatal diseases. Therefore, machine-learning and deep-learning-based heart disease prediction models hold substantial research value in the medical field with extensive potential applications and great significance. This article aims to establish a prediction model, utilizing the Deep Neural Network (DNN) algorithm, to cope with latent hazards associated with heart disease. A dataset from the Kaggle database was used for this approach. To assess the performance of the prediction model, evaluation indices including accuracy, recall_0, recall_1, and AUC, etc. were calculated for DNN and five comparative machine-learning algorithms. DNN eventually achieved outstanding performance with an accuracy of 0.76, recall_1 rate of 0.77, and AUC value of 0.84, respectively. The study concluded that DNN showed better average performance compared to other machine learning algorithms. The result could serve as an auxiliary strategy for heart disease diagnoses.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bixi Zhang
PY  - 2024
DA  - 2024/09/23
TI  - Deep Neural Network based Heart Disease Prediction
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 667
EP  - 680
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_70
DO  - 10.2991/978-94-6463-512-6_70
ID  - Zhang2024
ER  -