An Intelligent Heart Disease Prediction Framework Using Machine Learning and Deep Learning Techniques

An Intelligent Heart Disease Prediction Framework Using Machine Learning and Deep Learning Techniques

Nasser Allheeib, Summrina Kanwal, Sultan Alamri
Copyright: © 2023 |Pages: 24
DOI: 10.4018/IJDWM.333862
Article PDF Download
Open access articles are freely available for download

Abstract

Cardiovascular diseases (CVD) rank among the leading global causes of mortality. Early detection and diagnosis are paramount in minimizing their impact. The application of ML and DL in classifying the occurrence of cardiovascular diseases holds significant potential for reducing diagnostic errors. This research endeavors to construct a model capable of accurately predicting cardiovascular diseases, thereby mitigating the fatality associated with CVD. In this paper, the authors introduce a novel approach that combines an artificial intelligence network (AIN)-based feature selection (FS) technique with cutting-edge DL and ML classifiers for the early detection of heart diseases based on patient medical histories. The proposed model is rigorously evaluated using two real-world datasets sourced from the University of California. The authors conduct extensive data preprocessing and analysis, and the findings from this study demonstrate that the proposed methodology surpasses the performance of existing state-of-the-art methods, achieving an exceptional accuracy rate of 99.99%.
Article Preview
Top

2. Literature Review

Cardiovascular diseases (CVD) are a leading cause of global mortality. Early detection and diagnosis are critical for minimizing their impact. The medical community has increasingly turned to Machine Learning (ML) and Deep Learning (DL) for their capacity to extract valuable insights from data. ML and DL, when applied to classifying cardiovascular diseases, have shown significant potential in reducing diagnostic errors. While numerous studies have explored these techniques, there remains a critical need to thoroughly assess their effectiveness, address their limitations, and provide a path forward for improving accuracy.

Complete Article List

Search this Journal:
Reset
Volume 20: 1 Issue (2024)
Volume 19: 6 Issues (2023)
Volume 18: 4 Issues (2022): 2 Released, 2 Forthcoming
Volume 17: 4 Issues (2021)
Volume 16: 4 Issues (2020)
Volume 15: 4 Issues (2019)
Volume 14: 4 Issues (2018)
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing