Implementing critical machine learning (ML) approaches for generating robust discriminative neuroimaging representations using structural equation model (SEM)

• Autores: Panduro Ramirez, Jeidy Gisell, Rashad Baker, Mohammed, Lakshmi Padmaja, D., Puviarasi, R., Mann, Suman, Tiwari, Mohit, Abubakari Samori, Issah
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/5815
• Enlace del recurso: https://hdl.handle.net/20.500.12867/5815; http://doi.org/10.1155/2022/6501975
• Nivel de acceso: acceso abierto
• Materia: Neuroimaging; Machine Learning; https://purl.org/pe-repo/ocde/ford#2.02.03

Critical ML or CML is a critical approach development of the standard ML (SML) procedure. Conventional ML (ML) is being used in radiology departments where complex neuroimages are discriminated using ML technology. Radiologists and researchers found that sole decision by the ML algorithms is not accurate enough to implement the treatment procedure. Thus, an intelligent decision is required further by the radiologists after evaluating the ML outcomes. The current research is based on the critical ML, where radiologists’ critical thinking ability, IQ (intelligence quotient), and experience in radiology have been examined to understand how these factors affect the accuracy of neuroimaging discrimination. A primary quantitative survey has been carried out, and the data were analysed in IBM SPSS. The results showed that experience in works has a positive impact on neuroimaging discrimination accuracy. IQ and trained ML are also responsible for improving the accuracy as well. Thus, radiologists with more experience in that field are able to improve the discriminative and diagnostic capability of CML.