Explainable Classification of Patients with Primary Hyperparathyroidism Using Highly Imbalanced Clinical Data Derived from Imaging and Biochemical Procedures

Abstract

Primary hyperthyroidism (PHPT) is a common endocrine disorder characterized by hypercalcemia and elevated parathyroid hormone (PTH) levels. The most common cause is a single parathyroid adenoma, though the rest of the cases are due to multiglandular disease [double adenoma/hyperplasia]. The main focus driving this work is to develop a computer-aided classification model relying on clinical data to classify PHPT instances and, at the same time, offer explainability for the classification process. A highly imbalanced dataset was created using biometric and clinical data from 134 patients (six total features, 20.2% multiglandular instances). The features used by the current study are age, sex, max diameter index, number of deficiencies, Wisconsin index, and the reference variable indicating the type of PHPT. State-of-the-art machine learning (ML) classification algorithms were used in order to create trained prediction models and give predicted classifications based on all features/indexes. Of the ML models considered (Support Vector Machines, CatBoost, LightGBM, and AdaBoost), LightGBM was able to procure the best performing prediction model. Given the highly imbalanced nature of the particular dataset, oversampling was opted for, so as to increase prediction robustness for both classes. The ML model’s performance was then evaluated using common metrics and stratified ten-fold validation. The significance of this work is rooted in two axes: firstly, in the incorporation of oversampling to smooth out the highly imbalanced dataset and offer good prediction accuracy for both classes, and secondly, in offering an explainability aspect to an otherwise black-box ML prediction model. The maximum achievable accuracy for adenoma is 86.9% and for multigland disease 81.5%. Summarizing the above, this study demonstrates the potential for an ML approach to improve the diagnosis of PHPT and also highlights the importance of explainable artificial intelligence (AI).