TUBERCULOSIS DISEASE DIAGNOSIS BASED ON AN OPTIMIZED MACHINE LEARNING MODEL

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. /us, 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). /us, 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. /e obtained
experimental results highlight the efficiency of modified SVM classifier compared with other standard ones.