ENSEMBLE LEARNING AND UNCERTAINTY ESTIMATION FOR PREDICTIVE MODELING OF HYPOTHYROIDISM

Abstract

This study highlights the development of prediction of predictive model for predicting the hypothyroidism by leveraging the ensemble machine learning techniques that are combined with ensemble techniques The issues in associated with imbalanced and noisy datasets the study evaluated the use of SMOTE for balancing the class and PCA to reduce the dimensions. The models evaluated included AdaBoost, Bagging (Random Forest) and Blending (Stacking). With the focus on abilities to handle the complex medical data and increase the accuracy of prediction. The research highlights effectiveness of ensemble techniques methods in enhancing the reliability and accuracy of hypothyroidism prediction. Key findings shows that Blending (Stacking) shows best balance between precision, recall that provides superior performance as compared to others even when PCA and SMOTE are used. Moreover, the importance of uncertainty quantification in refining the predictive abilities is focused that evaluated its potential in high – stakes medical applications. The applications of PCA and SMOTE while beneficial in increasing the model performance also introduced some complexity and potential for over fitting.