ARCHIVES
Original Article
Efficient Predictive Scale for Computer-Aided Heart Disease Detection
Suyash Soni1
Abhay Sharma2
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
Published Online: November-December 2024
Pages: 11-12
Cite this article
No DOIReferences
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
2024
Matrix Representation of Graph Theory in Hydrocarbons
2024
A Review of Development of Chemical Sensors
2024
Towards Detection and Attribution of Cyber Attacks in IoT Enabled Cyber-Physical Systems
2024
Implementation of Waste Management System
2024
To Study the Role of Forest –Based Industries in Promoting Trade
2024
