Abstract: Purpose:Vaginal infections are prevalent causes of gynecological consultations. This study introduces and evaluates the efficacy of four Machine Learning algorithms in detecting vaginitis cases in southern Mexico. Methods:Utilizing Simple Perceptron, Naïve Bayes, CART, and AdaBoost, we conducted classification experiments to identify four vaginitis subtypes (gardnerella, candidiasis, trichomoniasis, and chlamydia) in 600 patient cases. Results:The outcomes are promising, with a majority achieving 100% accuracy in vaginitis identification. Conclusion:The successful implementation and high accuracy of these algorithms demonstrate their potential as valuable diagnostic tools for vaginal infections, particularly in southern Mexico. It is crucial in a region where health technology adoption lags behind, and intelligent software support is limited in gynecological diagnoses.
Keywords: Machine learning, gynecological pathologies, vaginitis, local dataset, correct identification
