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.
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