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Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model. | LitMetric

Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model.

J Healthc Eng

Department of Information Systems, College of Computer and Information Sciences, Jouf University, Jouf, Saudi Arabia.

Published: May 2022

AI Article Synopsis

Article Abstract

Computer science plays an important role in modern dynamic health systems. Given the collaborative nature of the diagnostic process, computer technology provides important services to healthcare professionals and organizations, as well as to patients and their families, researchers, and decision-makers. Thus, any innovations that improve the diagnostic process while maintaining quality and safety are crucial to the development of the healthcare field. Many diseases can be tentatively diagnosed during their initial stages. In this study, all developed techniques were applied to tuberculosis (TB). Thus, we propose an optimized machine learning-based model that extracts optimal texture features from TB-related images and selects the hyper-parameters of the classifiers. Increasing the accuracy rate and minimizing the number of characteristics extracted are our goals. In other words, this is a multitask optimization issue. A genetic algorithm (GA) is used to choose the best features, which are then fed into a support vector machine (SVM) classifier. Using the ImageCLEF 2020 data set, we conducted experiments using the proposed approach and achieved significantly higher accuracy and better outcomes in comparison with the state-of-the-art works. The obtained experimental results highlight the efficiency of modified SVM classifier compared with other standard ones.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9041161PMC
http://dx.doi.org/10.1155/2022/8950243DOI Listing

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