Title: Neoteric machine learning approaches to diagnose the state of carotid artery

Authors: Hariharan Anantharaman; Navendu Chaudhary; Vimal Raj

Addresses: Faculty of Computer Studies, Symbiosis International (SIU) (Deemed to be University), Hinjewadi, Pune, Maharashtra, India ' Symbiosis Institute of Geoinformatics (SIG), Symbiosis International (SIU) (Deemed to be University), Model Colony, Pune, Maharashtra, India ' Narayana Institute of Cardiac Sciences, 258/A, Hosur Rd., Bommasandra Industrial Area, Bengaluru, Karnataka, India

Abstract: This paper aims to develop machine learning models based on the information, data elements, and images captured on a carotid ultrasound, initiated with the capture, collation, and compilation of comprehensive carotid ultrasound reports of patients. Next is the analysis, extraction, cleaning, and compilation of data for the development of the models. In this neoteric approach, a set of supervised algorithms and image-deep learning algorithms are implemented, and different models are built and tested. The performance of all the models is par excellence, with the majority delivering accuracy over 75%. All the models, based on the varied machine learning algorithms, delivered acceptable and consistent accuracy - few models have even surpassed and delivered superior accuracy.

Keywords: classifier; supervised learning; carotid ultrasound; stenosis; machine learning; deep learning.

DOI: 10.1504/IJMEI.2024.138292

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.3, pp.274 - 286

Received: 10 Sep 2021
Accepted: 26 Feb 2022
Published online: 01 May 2024 *

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