Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm
By Zeinab Arabasadi, Roohallah Alizadehsani, Mohamad Roshanzamir, Hossein Moosaei, Ali Asghar Yarifard. Comput. Methods Programs Biomed., 2017
456 citations recorded by the source corpus.
Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the proposed method is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network. Making use of such methodology, we achieved accuracy, sensitivity and specificity rates of 93.85%, 97% and 92% respectively, on Z-Alizadeh Sani dataset.
Subfields: Cardiology and Cardiovascular Medicine, Computer Vision and Pattern Recognition, Health Information Management
View Paper: https://www.semanticscholar.org/paper/53c5972eec92b21f213f2c1d6c2b6e8f23882700
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In Computer Vision and Pattern Recognition
Citations in the corpus
- Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques (2021, methodology): “Angiography is considered as the most precise and accurate method for the prediction of cardiac artery disease (CAD) [9], but it is very costly which makes it less accessible to low-income families.”