Optimizing Hypothyroid Diagnosis with Physician-Supervised Feature Reduction using Machine Learning Techniques

Seven out of ten women in India are allegedly affected by thyroid difficulties, making hypothyroid diseases a major health concern among women. It is essential to detect hypothyroidism early in order to avoid problems and enhance patient outcomes. ML algorithms are a powerful tool for medical data analysis, and a number of researchers have employed different ML algorithms for hypothyroid categorization. In this study, we suggest the use of three supervised ML algorithms for hypothyroid classification: Random Forest, Logistic Regression, and Light-GBM Classifier, together with feature reduction. The Light-GBM Classifier achieves an accuracy of99.85% for 25 characteristics and 99.92% for 17 attributes, respectively. The inclusion of physician assistance to lower the number of variables has no significant effect on the accuracy of the Light-GBM classifier, indicating that fewer features may be necessary for reliable hypothyroid prediction. Our findings indicate that the Light-GBM classifier has the potential to be a valuable tool for hypothyroid prediction; however, additional validation and assessment on bigger and more varied datasets are required to assure its reliability and generalizability.