Katherine H Hall, Claire Amos, Chrystal Jaye, Jessica Young
Successful Psychological Strategies of Experienced Chronic Fatigue Patients: A Qualitative Study
Journal of patient experience
thyroid disease classification plays crucial role early diagnosis effective treatment thyroid disorders machine learning ml techniques demonstrated remarkable potential domain offering accurate efficient diagnostic tools reallife datasets imbalanced characteristics hamper overall performance classifiers existing data balancing techniques process whole dataset time sometimes causes overfitting underfitting however complexity ml models often referred black boxes raises concerns interpretability clinical applicability paper presents comprehensive study focused analysis interpretability various ml models classifying thyroid diseases work first applied new databalancing mechanism using clustering technique analyzed performance different ml algorithms address interpretability challenge explored techniques model explanation feature importance analysis using explainable artificial intelligence xai tools globally well locally finally xai results validated domain experts experimental results shown proposed mechanism efficient diagnosing thyroid disease explain models effectively findings contribute bridging gap adopting advanced ml techniques clinical requirements transparency accountability diagnostic decisionmaking