- Home
- Archives
- 2024
- Vol. 4 Issue 6
- Article
Efficient Predictive Scale for Computer-Aided Heart Disease Detection
Authors
1*M. Tech Student, Department of Computer Science and Engineering, Maulana Azad National Institute of Technology Bhopal, Madhya Pradesh, India. 2**Prof. Department of Computer Science and Engineering, Maulana Azad National Institute of Technology Bhopal, Madhya Pradesh, India
Article Information
- DOI
- No DOI
- Published In
- Volume 4, Issue 6 (2024)
- Published Online
- November-December 2024
- Pages
- 11-12
- Article Type
- Original Article
- Check for updates
- No DOI available for cross-reference
References
1. Estes, C.; Anstee, Q.M.; Arias-Loste, M.T.; Bantel, H.; Bellentani, S.; Caballeria, J.; Colombo, M.; Craxi, A.; Crespo, J.; Day, C.P.; et
al. Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period
2016–2030. J. Hepatol. 2018, 69, 896–904.
2. Drożdż, K.; Nabrdalik, K.; Kwiendacz, H.; Hendel, M.; Olejarz, A.; Tomasik, A.; Bartman, W.; Nalepa, J.; Gumprecht, J.; Lip, G.Y.H.
Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: A machine learning
approach. Cardiovasc. Diabetol. 2022, 21, 240.
3. Murthy, H.S.N.; Meenakshi, M. Dimensionality reduction using neuro-genetic approach for early prediction of coronary heart disease.
In Proceedings of the International Conference on Circuits, Communication, Control and Computing, Bangalore, India, 21–22 November
2014; pp. 329–332.
4. Benjamin, E.J.; Muntner, P.; Alonso, A.; Bittencourt, M.S.; Callaway, C.W.; Carson, A.P.; Chamberlain, A.M.; Chang, A.R.; Cheng, S.;
Das, S.R.; et al. Heart disease and stroke statistics—2019 update: A report from the American heart association. Circulation 2019, 139,
e56–e528.
5. Shorewala, V. Early detection of coronary heart disease using ensemble techniques. Inform. Med. Unlocked 2021, 26, 100655
al. Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period
2016–2030. J. Hepatol. 2018, 69, 896–904.
2. Drożdż, K.; Nabrdalik, K.; Kwiendacz, H.; Hendel, M.; Olejarz, A.; Tomasik, A.; Bartman, W.; Nalepa, J.; Gumprecht, J.; Lip, G.Y.H.
Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: A machine learning
approach. Cardiovasc. Diabetol. 2022, 21, 240.
3. Murthy, H.S.N.; Meenakshi, M. Dimensionality reduction using neuro-genetic approach for early prediction of coronary heart disease.
In Proceedings of the International Conference on Circuits, Communication, Control and Computing, Bangalore, India, 21–22 November
2014; pp. 329–332.
4. Benjamin, E.J.; Muntner, P.; Alonso, A.; Bittencourt, M.S.; Callaway, C.W.; Carson, A.P.; Chamberlain, A.M.; Chang, A.R.; Cheng, S.;
Das, S.R.; et al. Heart disease and stroke statistics—2019 update: A report from the American heart association. Circulation 2019, 139,
e56–e528.
5. Shorewala, V. Early detection of coronary heart disease using ensemble techniques. Inform. Med. Unlocked 2021, 26, 100655
Related Articles
Share Article
*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.