Use of machine learning to diagnose somatic symptom disorder: Are the biomarkers beneficial for the diagnosis?

Abstract

Objectives: We used machine learning to incorporate three types of biomarkers (respiratory sinus arrhythmia, RSA; skin conductance, SC; finger temperature, FT) for examining the performance of diagnosing somatic symptom disorder (SSD).

Methods: We recruited 97 SSD subjects and 96 controls without psychiatric history or somatic distress. The values of RSA, SC and FT were recorded in three situations (resting state, under a cognitive task and under paced breathing) and compared for the two populations. We used machine learning to combine the biological signals and then applied receiver operating characteristic curve analysis to examine the performance of diagnosing SSD regarding the distinct indicators and situations. Subgroup analysis for subjects without depression/anxiety was also conducted.

Results: FT was significantly different between SSD patients and controls, especially in the resting state and under paced breathing. However, the biomarkers (0.75-0.76) did not reveal an area under the curve (AUC) comparable with the psychological questionnaires (0.86). Combining the biological and psychological indicators gave a high AUC (0.86-0.92). When excluding individuals with depression/anxiety, combining three biomarkers (0.79-0.83) and adopting psychological questionnaires (0.78) revealed a similar AUC.

Conclusions: The performance of RSA/SC/FT was unsatisfactory for diagnosing SSD but became comparable when excluding comorbid depression/anxiety.