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 algorithmsâ€â€including decision trees, random forests, support vector machines, and gradient boostingâ€â€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.
Thyroid disorders represent a significant and growing public health concern worldwide, affecting millions of individuals across diverse age groups and populations [1]. The thyroid gland plays a crucial role in maintaining metabolic balance, cardiovascular stability, and neurological function through the secretion of hormones such as thyroxine (T4) and triiodothyronine (T3). Even subtle deviations in hormone levels can lead to systemic metabolic disruptions, influencing various physiological processes [2]. Common thyroid abnormalities include hypothyroidism, hyperthyroidism, goiter, and autoimmune conditions such as Hashimoto’s thyroiditis and Graves’ disease [3]. The global increase in thyroid dysfunction can be attributed to genetic predisposition, iodine deficiency, environmental exposure, and lifestyle-related factors [4]. Advances in medical testing, a substantial proportion of thyroid abnormalities remain undiagnosed or are detected only after clinical manifestation. This diagnostic gap highlights the necessity of developing predictive tools capable of identifying early-stage disorders before the onset of severe complications [5].
Traditional diagnostic methods for thyroid dysfunction primarily rely on biochemical tests such as thyroid-stimulating hormone (TSH), free T3, and free T4 assays, along with imaging studies and clinical evaluations [6]. Although these methods are widely used, they often fail to capture the complex, nonlinear relationships among multiple physiological indicators [7]. Variations in hormone levels due to age, gender, medication, and comorbid conditions further complicate diagnosis, leading to potential misclassification [8]. Static threshold-based interpretation may not reflect the dynamic interactions governing thyroid physiology [9]. As a result, conventional approaches sometimes yield inconclusive or inconsistent results, particularly in cases of subclinical thyroid disorders where symptoms are mild or nonspecific. Hence, there was a growing demand for advanced diagnostic models that can analyze multidimensional data and provide more accurate, data-driven insights into thyroid health assessment [10].
Machine learning has emerged as a transformative paradigm in the field of medical diagnostics, offering powerful capabilities to uncover hidden patterns within complex datasets [11]. Unlike traditional statistical methods that depend on predefined assumptions, machine learning algorithms can learn intricate, nonlinear relationships from raw data and adapt to evolving clinical patterns [12]. In thyroid disorder diagnosis, machine learning models such as support vector machines, random forests, decision trees, and artificial neural networks have demonstrated remarkable accuracy in distinguishing between normal and pathological conditions [13]. These models enable automated classification based on large-scale clinical and biochemical datasets, reducing dependence on manual interpretation and improving diagnostic consistency [14]. The integration of feature selection and dimensionality reduction techniques ensures that only the most relevant parameters contribute to predictive modeling, enhancing both computational efficiency and interpretability [15].
