Predicting Chronic Hyperplastic Candidiasis in the Tongue using Machine Learning: A Study of 186 Cases

Abstract

Introduction This study examines the distribution of 186 Chronic Hyperplastic Candidiasis (CHC) cases verified by biopsy within the oral cavity, focusing on the prevalence in the tongue (72 cases) versus other oral locations (114 cases). Methods Utilizing the Random Forest Regressor (RFR), a robust machine learning algorithm, we analyze 16 unique risk factors to predict CHC incidence in the tongue. Linear regression is employed to evaluate the model's performance. Results The RFR demonstrates high accuracy in predicting CHC presence in various oral sites. The study highlights the impact of risk factors on CHC prevalence and the importance of CHC's location in the oral cavity for tailored diagnostic and treatment approaches. The findings suggest the Random Forest Regressor's potential as a tool for healthcare professionals in the early identification and diagnosis of CHC, enhancing disease understanding and improving patient care. Conclusion The RFR proves effective in predicting CHC occurrence in different oral areas. The clinical significance of Machine Learning method usage lies in the optimal evaluation of true pathogenetic factors and their relation patterns for CHC development in the tongue. Notably, most tongue CHC patients were non-smokers (63.9%), and female patients slightly outnumbered males (54.2%), challenging the common association of CHC with male smokers. A significant association exists between gastroesophageal reflux and tongue CHC (p=0.01), and a similar trend is noted for thyropathy in lingual lesions compared to other CHC locations (p=0.09). These findings underscore the necessity for clinicians to consider negative cultivations in lingual CHC cases (20.8% of cases), ensuring comprehensive evaluation and treatment.