Machine Learning–Based Predictive Model for Early Diagnosis of Thyroid Disorders

Abstract

Thyroid disorders represent a major global health concern, with an increasing prevalence due to lifestyle changes, environmental factors, and genetic predisposition. Early detection of thyroid dysfunction, including hypothyroidism, hyperthyroidism, and subclinical variants, was critical for effective clinical management and prevention of systemic complications. Traditional diagnostic approaches, which rely primarily on biochemical markers and clinical evaluation, often fail to capture the subtle nonlinear relationships among physiological indicators, leading to delayed or inaccurate diagnosis. To address these challenges, machine learning-based predictive models provide a powerful framework for integrating multidimensional clinical and biochemical data to achieve more accurate, automated, and interpretable diagnostic outcomes. This chapter presents a comprehensive exploration of the development, optimization, and evaluation of machine learning models for the early diagnosis of thyroid disorders. Emphasis was placed on dataset preprocessing, feature engineering, and techniques for addressing class imbalance, such as Synthetic Minority Oversampling Technique (SMOTE), to enhance model robustness. Comparative analyses of algorithms—including decision trees, random forests, support vector machines, and gradient boosting—are conducted to identify the most efficient model architecture for clinical application. Performance metrics such as accuracy, sensitivity, specificity, and ROC-AUC are employed to ensure balanced evaluation across all diagnostic categories. The chapter highlights the importance of model explainability and interpretability to support clinical trust and decision-making. The integration of machine learning methodologies with endocrinological diagnostics represents a transformative advancement in predictive medicine. By combining clinical insights with computational intelligence, the proposed framework enables the identification of subtle patterns in patient data, facilitating early intervention and reducing morbidity and mortality associated with thyroid dysfunctions. This approach not only improves diagnostic precision but also lays the groundwork for personalized treatment strategies and real-time decision support systems in medical practice.

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