Abstract
Accurate classification of Cushing's Syndrome (CS) plays a critical role in providing the early and correct diagnosis of CS that may facilitate treatment and improve patient outcomes. Diagnosis of CS is a complex process, which requires careful and concurrent interpretation of signs and symptoms, multiple biochemical test results, and findings of medical imaging by physicians with a high degree of specialty and knowledge to make correct judgments. In this article, we explore the state of the art machine learning algorithms to demonstrate their potential as a clinical decision support system to analyze and classify CS to facilitate the diagnosis, prognosis, and treatment of CS. Prominent algorithms are compared using nested cross-validation and various class comparison strategies including multiclass, one vs. all, and one vs. one binary classification. Our findings show that Random Forest (RF) algorithm is most suitable for the classification of CS. We demonstrate that the proposed approach can classify CS with an average accuracy of 92% and an average F1 score of 91.5%, depending on the class comparison strategy and selected features. RF-based one vs. all binary classification model achieves sensitivity of 97.6%, precision of 91.1%, and specificity of 87.1% to discriminate CS from non-CS on the test dataset. RF-based multiclass classification model achieves average per class sensitivity of 91.8%, average per class specificity of 97.1%, and average per class precision of 92.1% to classify different subtypes of CS on the test dataset. Clinical performance evaluation suggests that the developed models can help improve physicians' judgment in diagnosing CS.
Highlights
C USHING’s Syndrome (CS) is a potentially lethal disorder caused by abnormally high levels of cortisol hormone, first described in 1912 by Harvey Cushing [1], [2]
We explore the state of the art machine learning (ML) algorithms, and demonstrate their usefulness as a clinical decision support system to evaluate results of the medical tests, and predict CS to facilitate the diagnosis and prognosis of CS
The selected algorithm was used to build classification models with different class comparison strategies and feature sets which were selected after discussing it with physicians specialized in CS as to how useful it will be in clinical diagnosis and prognosis
Summary
C USHING’s Syndrome (CS) is a potentially lethal disorder caused by abnormally high levels of cortisol hormone, first described in 1912 by Harvey Cushing [1], [2]. The median age is 41.4 years, and the female to male ratio is 4 to 1 [3], [4] It may stem from prolonged intake of glucocorticoids-steroid hormones that are chemically similar to natural cortisol, such as antiinflammatory medications prescribed for asthma, rheumatoid arthritis, lupus, and other inflammatory diseases. Such hormones may be taken after an organ transplant to suppress the immune system and prevent organ rejection. Cushing Disease, a form of CS, is the most common cause of excess endogenous cortisol production by the adrenal glands. It is called ectopic ACTH production because it is produced somewhere other than the pituitary gland
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