Optimizing Thyroid Disorder Diagnosis: An ensemble Machine Learning Techniques

Thyroid disorders, including hormonal abnormalities, have a profound effect on human health by affecting metabolism and physiological processes in general This work equips a complex learning algorithm combining decision trees, XGBoost, and logistic regression to overcome the challenges of reliably diagnosing these diseases under different experimental conditions Research also ensures that they can be used reliable: average, worst, and best. The ensemble method performs exceptionally well, with an outstanding diagnostic accuracy of 0.94 across data sets. This model raises the accuracy of medical diagnosis by using different algorithms and reducing their errors. The results show significant progress in integrating machine learning into healthcare systems to improve patient outcomes, highlighting the potential of sophisticated computational techniques to improve clinical processes.