Early Diagnosis of Hypothyroidism in the Eastern Province of Saudi Arabia Using Computational Intelligence Techniques

Hypothyroidism is one of the most common yet underdiagnosed medical conditions in Saudi Arabia. It is more prevalent in aged and pregnant women as well as in patients with diabetes and sleep apnea. Hypothyroidism is characterized by the thyroid gland producing inadequate thyroid hormones, which might result in other chronic illnesses if left untreated. For this reason, this study proposes machine learning techniques to preemptively diagnose this disease using a straightforward clinical dataset from Saudi Arabia. Given the data size, this work serves as proof of concept. Algorithms such as KNN, SVM, Gradient boosting, and soft voting ensemble classifier were chosen for their promising performance in the proactive diagnosis of hypothyroidism and associated diseases compared to other algorithms in literature. The best performing model was the soft voting ensemble classifier, which achieved an accuracy of 94.7%. SVM, KNN, and XGBoost achieved 94%, 93.42%, and 92.1% accuracies, respectively. These results were obtained using 10 fold cross validation and forward sequential feature selection.