Prediction of atherosclerosis diseases using biosensor-assisted deep learning artificial neuron model

In the present medical era, the major cause of the rise in death rate worldwide is atherosclerosis disease and this diagnosis is complicated because initial signs are unattended. To reduce the costs of treatment and prevent serious events, it is necessary to improve the prediction accuracy of cardiovascular diseases during plaque formation. This proposal is intended to create a support system for the biosensor-assisted deep learning concepts for detecting atherosclerosis disease. With the clinical data, this mathematical model can predict heart disease based on deep learning-assistedk-means geometric distribution artificial neuron model. The atherosclerotic plaque formation mathematical model explains the early atherosclerotic lesion development in a more accurate manner. Further, the creation of the atherosclerotic plate, the test performs numerical simulations with idealized two-dimensional carotid artery bifurcation geometry. The proposed system has been analyzed using a variety of similarity tests such as the coefficient Matthews's correlation (CMC). Furthermore, the results have reached 95.66% accuracy and 0.93 CMC, which are significantly higher than published conventional research.

Automated summary

In the current medical era, the major cause of the global rise in death rate is atherosclerosis disease, a complex diagnosis with early signs often overlooked. To improve the prediction accuracy of cardiovascular diseases, a support system for biosensor-assisted deep learning concepts has been proposed to detect atherosclerosis disease. Through clinical data, a mathematical model based on deep learning-assisted k-means geometric distribution artificial neuron model can accurately predict heart disease during atherosclerotic plaque formation.