Determining the Effect of Fear of Illness and Virus Evaluation and Quality of Life on Diagnosis of Social Phobia in Patients With Chronic Disease: Using Machine Learning Approaches.

Abstract

Aim: While the SARS-CoV-2 pandemic and other epidemics continue, individuals with chronic diseases and those over the age of 60 are most affected by the psychological effects. This research is the first and most crucial study comparing the quality of life, physical activities, fear of disease and virus evaluation, and social phobia in chronic patients and healthy individuals, and modeling the classification of social phobia using the machine learning approach.

Methods: The quantitative study used STROBE guidelines for the correlational and cross-sectional design. The research questionnaire was designed in four parts: a personal information form, the Liebowitz Social Phobia Scale, the Fear of Illness and Virus Evaluation Scale, and the Quality of Life Scale (EUROHIS-WHOQOL-8). Different algorithms were examined using the machine learning approach to classify social phobia. More participants were reached than the calculated sample size (n = 1068) using simple random sampling, and the final sample size was 1235.

Results: Patients with chronic diseases had lower physical activity levels and quality of life scores. Patients with chronic diseases (n=728) had higher Fear of Illness and Virus Evaluation Scale-35 scores and Liebowitz Social Phobia Scale-24 scores compared to healthy participants (n=507) and lower physical activity levels (3.901 ± 3.035) and quality of life scores (29.016 ± 4.782). Two algorithms (K-nearest neighbors and support vector machine algorithm) provided the best performance. In support vector machine algorithm, Fear of Illness and Virus Evaluation Scale-35 was the most critical feature in classifying social phobia. Physical activity level and Liebowitz Social Phobia Scale seem to be positively related in k-nearest neighbors.

Conclusion: The model is essential for identifying and understanding social phobia factors in patients with chronic diseases. Support vector machine algorithm is an algorithm that is preferred for identifying patients at risk of fear and will facilitate follow-up when integrated into smartphone applications.