Prediction of cardiovascular risk by measuring carotid intima media thickness from an ultrasound image for type II diabetic mellitus subjects using machine learning and transfer learning techniques

• Published in: The Journal of supercomputing Vol. 77; no. 9; pp. 10289 - 10306
• Main Authors: Lakshmi Prabha, P., Jayanthy, A. K., Prem Kumar, C., Ramraj, Balaji
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2021; Springer Nature B.V
• Subjects: Blood; Cardiovascular disease; Compilers; Computer Science; Demographic variables; Diabetes; Heart diseases; Interpreters; Machine learning; Mathematical analysis; Mobile and Intelligent Sensing on High Performance Computing; Multilayer perceptrons; Parameters; Processor Architectures; Programming Languages; Risk; Sensitivity; Support vector machines; Thickness; Ultrasonic imaging; Carotid intima media thickness; Framingham risk score; Transfer learning technique; Machine learning; Pearson correlation

Cardiovascular disease (CVD) is a fatal disease that causes increased death in developing and developed nations. Among the various reasons, the increase in carotid intima media thickness (CIMT) is also a significant reason for CVD. It is expected to increase the death rate due to CVD up to 24.2 mill...