Enhancing Hypothyroid Severity Classification through Bayesian Optimization of Machine Learning Models

Objective: A person with undiagnosed or ineffectively assessed hypothyroidism may lead to serious metabolic and cardiovascular problems. Existing diagnostic methods mainly focus on binary classification (hypothyroid vs. non-hypothyroid) and frequently oversee the need to stratify patients by severity level, which is serious for guiding treatment intensity. This study proposes to develop a machine learning outline that not only expects the presence of hypothyroidism but also categorizes its severity level, using Bayesian Optimization to perfect model performance for enhanced diagnostic precision. Methods: A clinical dataset comprising thyroid function indicators such as TSH, T3, T4, FTI, and patient demographic/clinical traits was used to figure a multi-class classification model for hypothyroid severity levels like mild, moderate and severe hypothyroidism. Data preprocessing comprises of handling missing values, imbalanced dataset, and feature selection. Several baseline classifiers including Random Forest, K-Nearest Neighbour and Support Vector Machine were trained, and Bayesian Optimization was employed to methodically search the hyperparameter space of these models, optimizing parameters like maximum depth of the tree, minimum number of leaves, minimum sample split, number of estimators, selection of criterion. Model performance metrics was assessed using accuracy, precision, recall, F1-score with cross-validation confirming robustness against overfitting. Findings: The Bayesian-optimized models constantly outperformed their grid-search on randomized search cross validation, attaining better classification accuracy and more balanced precision and recall across severity classes. The optimized ensemble model established strong discriminative capability in clinical mild, moderate and severe hypothyroid cases, which are frequently clinically unclear. Feature importance analysis was revealed that TSH and FTI levels were the most significant predictors of severity, positioning with recognized clinical understanding. Novelty: Distinct prior studies that mainly address hypothyroidism as a binary classification problem, this study introduces a severity level prediction, offering fine tunes diagnostic support. The combination of Bayesian Optimization for hyperparameter tuning rather than conservative grid or random search CV, it provided a more efficient and adaptive optimization process, dropping computational cost while improving predictive accuracy. This combination of severity level-based organization and optimized hyperparameter tuning method represents a novel contribution towards building clinically meaningful, precise, and effective hypothyroid diagnostic support systems.