Diagnosis of Cushing's syndrome with generalized linear model and development of mobile application

Abstract

Background: Cushing syndrome (CS) is a rare endocrine disorder characterized by excessive secretion of glucocorticoids, leading to a variety of clinical manifestations, comorbidities, and increased mortality despite treatment. Despite advances in imaging modalities and biochemical testing, the diagnosis and management of CS remains challenging. Several tests are used to confirm the diagnosis of CS, including urinary free cortisol measurements, dexamethasone suppression tests (1 mg, 2 mg, and 8 mg), and nocturnal salivary cortisol measurements. However, each of these tests has some limitations, making the diagnosis of CS.

Methods: In this paper, we explore the potential of state-of-the-art machine learning algorithms as a clinical decision support system for analyzing and classifying CS. Our aim is to use advanced machine learning methods to analyze the accuracy rates of diagnostic tests and identify the most sensitive tests for diagnosing CS.

Results: In this study, we performed binary classification based on data from 278 patients with CS (CS+) and 220 healthy patients (CS-). We developed a linear mathematical model with high predictive ability, achieving a classification accuracy of 97.03% and a Kappa value of 94.05%. The correlation graph shows that CS has strong positive relationships with 2 mg (78.8%), 1 mg (76.9%), and mc (72.1%), and moderate positive correlations with 8 mg (45%) and saliva (45.4%). In contrast, gender has almost no correlation with CS, so it was removed from the dataset. As a result, the model achieves an overall classification accuracy of 97.03%. Finally, we converted the linear model into a mobile application for use by specialist doctors in the field of endocrinology.

Conclusion: Traditional diagnostic methods can be time-consuming and require specialized medical expertise. Recently, advances in machine learning and mobile technology have opened new avenues for improving diagnostic accuracy and accessibility. This study explores the integration of machine learning algorithms into a mobile application designed to assist healthcare professionals and patients in the diagnosis of CS.

Keywords: Cushing Diagnosis Mobile App; Cushing disease; Cushing syndrome; ectopic Cushing syndrome; machine learning.

Figure 1.

Correlation graph saliva:salivary cortisol, mg1 = 1 mg DST, mg2 = 2 mg DST, mg8 = 8 mg DST, mc = midnight cortisol, ufc = urinary free cortisol, CS = Cushing Syndrome (Since the first letters in a computer program cannot start with a number, “mg” was added before the numbers.)

Figure 2.

GLM coefficients: saliva = salivary cortisol, bc = Bazal Cortisol, bacth = Bazal ACTH, mg1 = 1 mg DST, mg2 = 2 mg DST, mg8 = 8 mg DST, mc = midnight cortisol, ufc = urinary free cortisol (Since the first letters in a computer program cannot start with a number, “mg” was added before the numbers.). DST = dexamethasone suppression test, GLM = Generalized Linear Models.

Figure 3.

Test results of GLM model. GLM = Generalized Linear Models.

Figure 4.

Image of the mobile application interface bc = Bazal Cortisol, bacth = Bazal ACTH, saliva = salivary cortisol, mg1 = 1 mg DST, mg2 = 2 mg DST, mg8 = 8 mg DST, mc = midnight cortisol, ufc = urinary free cortisol (Since the first letters in a computer program cannot start with a number, “mg” was added before the numbers.). DST = dexamethasone suppression test.