Abstract:
As of the most current information, thyroid illnesses remain widespread on a worldwide
scale. The rate of hypothyroidism among Americans is around 4.6%, with a higher
incidence seen in women and older individuals. Meanwhile, hyperthyroidism affects
around 1.3% of the population. Globally, iodine deficiency continues to be a primary factor
contributing to thyroid problems, especially in areas with limited availability of iodineenriched diet or supplements. Concerns about thyroid disease should be raised for human
health due to the thyroid gland's involvement in regulating human metabolism and its
essential impact on overall well-being. Through this research on the thyroid, 3 types of
thyroid disease can be identified (hyperthyroid, hypothyroid, negative). To do this research
I used several machine learning models (Logistics Regression, Decision Tree, Naive
Bayes, Random Forest, Bagging Classifier, XGBoost, SGD, AdaBoost, Grid Search
Bagging, Grid Search Logistic, Support Vector Machine (SVM), ANN) etc. Among all the
models some of the best performing models are: Bagging accuracy 99.04%, XGBoost
accuracy 98.78%, Random Forest accuracy 98.67%, Grid Search Bagging accuracy
98.54%. Among these models, the Bagging classifier model performed best, so here
selected this model as the best and final model. Then added Explainable AI to the final
model. Explainable AI's job here is to explain how the model is making decisions based on which features to predict results. Previous papers have dealt with 1 or 2 classes. worked on 3 classes (hyperthyroid, hypothyroid, negative) at once to overcome that limitation, So,
here can say that the time spent between these tasks is reasonable.
