Predicting Thyroiditis Risk Using Artificial Neural Networks: A Multifactorial Approach

Thyroiditis, an inflammatory condition affecting thyroid function, can lead to significant health complications if undiagnosed or untreated. Identifying high-risk individuals for timely intervention is critical, yet conventional diagnostic methods struggle to integrate the complex, multifactorial data associated with thyroiditis risk factors. This study explores the application of artificial neural networks (ANNs) in analyzing thyroiditis risk factors, leveraging their ability to model non-linear relationships and handle high-dimensional data. Using a dataset of clinical and lifestyle attributes, including genetic predisposition, iodine intake, autoimmune disorders, medication usage, age, gender, and lifestyle factors, we developed an ANN-based predictive model to assess thyroiditis risk. The data pre-processing phase involved normalizing features, handling missing data, and implementing feature selection techniques to reduce model complexity while retaining significant predictors. The ANN architecture was optimized through hyperparameter tuning, and we experimented with various network structures, including deep and shallow models, to achieve optimal performance. Training was performed on a subset of data, while another portion was retained for validation and testing to evaluate the model's accuracy and generalization ability. Results indicated that the ANN model achieved high accuracy in predicting individuals at risk for thyroiditis, surpassing traditional logistic regression and decision tree classifiers. Key variables influencing the model’s prediction included autoimmune disease presence, iodine levels, family history, and specific medications, aligning with established clinical findings on thyroiditis risk factors. Moreover, the model revealed complex interactions between lifestyle factors and genetic predisposition, emphasizing the importance of multifactorial analysis in disease prediction. This research demonstrates the potential of ANNs as a valuable tool for early identification of thyroiditis risk. By providing a more nuanced understanding of risk factor interactions, ANN-based models could support clinicians in identifying at-risk patients and tailoring preventive interventions. Future work will involve expanding the dataset to improve model robustness and exploring interpretability techniques to elucidate ANN decision-making processes, thereby increasing their applicability in clinical settings.