Genomic Disease Diagnosis Using Support Vector Machines

The diagnosis of extrapulmonary tuberculosis (EPTB) presents significant challenges due to its varied manifestations and the limitations of conventional diagnostic methods.This study proposes the use of a Support Vector Machine (SVM) model to classify EPTB using gene expression data obtained from the GSE83456 dataset.By leveraging advanced machine learning techniques, we aim to enhance the accuracy and reliability of EPTB diagnosis.Our model was trained and validated using this dataset, achieving a remarkable accuracy.This paper outlines the methodology, results, and potential implications of using SVM models in the diagnosis of EPTB, highlighting a significant improvement over traditional diagnostic methods.