Using Artificial Neural Network to Predict Predisposing to Vulvovaginal Candidiasis among Vaginitis Cases

Abstract

Background: Vulvovaginal candidiasis (VVC) is a common fungal infection caused by Candida species in the female genital tract. Objectives: This study attempts to predict predisposition to VVC related to risk factors and clinical symptoms among vaginitis cases using the artificial neural network (ANN) model. Methods: This cross-sectional study was performed on 250 women referred to gynecology clinics in Birjand, Iran. A questionnaire was used to record participants' demographic information. Swabs were used for wet mounts and culture. Candida species were identified by morphological and physiological methods. The performance of the optimal neural network model was assessed by the sensitivity, specificity, and accuracy area under the ROC curve (AUC). Descriptive statistics were used for the statistical description of data, and chi-square test, t-test, and ANN analysis using SPSS application tools (Statistical Product and Service Solutions) version 22 software at 0.05 significant level. Results: The prevalence of vulvovaginal candidiasis was 41.0%, and Candida albicans was the most frequently identified species (55.9%). The descriptive statistics (chi-square test and t-test) revealed no significant difference between the frequencies of Candida infection with demographic factors and clinical presentations. However, factors such as abortion history, number of sexual intercourse, dyspareunia, education, natural vaginal delivery (NVD), and lower abdominal pain included in our ANN model had significant differences (P < 0.05). Conclusions: The result of the ANN model revealed that using demographic factors and clinical symptoms can predict VVC infection. Therefore, this model can identify the effect of the clinical presentations and symptoms of infection.