Frontiers in artificial intelligence · 2026
Prediction of female reproductive tract infections risk among college-going young adult women in Delhi using explainable artificial intelligence.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ghosh Joyeta, Kant Ravi, Bhardwaj Priya, Muralidhar Sumathi, Christodoulides Myron, Taneja Jyoti
Аннотация
INTRODUCTION: Reproductive tract infections (RTIs) and sexually transmitted infections (STIs) pose a substantial economic burden and public health concern in developing countries such as India, where inadequate early detection and prevention strategies often lead to increased morbidity, mortality, stigma, cancer and adverse reproductive health outcomes in both men and women.
METHODS: The present cross-sectional study employed machine-learning models to predict the risk of RTI/STI among young women in Delhi, India, by analysing self-reported symptoms along with demographic, reproductive health, lifestyle, and hygiene-related factors. The data were analysed using multiple machine learning algorithms, including decision trees, random forest, k-nearest neighbors (KNN), stochastic gradient descent (SGD), and XGBoost. Prior to analysis, the data was preprocessed through different steps including missing-value imputation, outlier removal using the interquartile range (IQR) method, and the synthetic minority oversampling technique (SMOTE) to address class imbalance.
RESULTS: A comparative analysis of model performance revealed that the XGBoost algorithm achieved the highest accuracy (89.1%) and the greatest area under receiver operating characteristics curve (ROC-AUC = 0.935), with random forest performing strongly as the second-best model (accuracy = 87.6%, ROC-AUC = 0.921), demonstrating its strong ability to capture non-linear relationships among RTI risk factors. SHAP (SHapley Additive exPlanations) analysis identified family medical history, personal RTI history, and menstrual cycle characteristics as the most influential predictors.
DISCUSSION: The findings underscore the potential of machine learning-driven predictive models in RTI risk stratification, screening support, and hypothesis generation.
METHODS: The present cross-sectional study employed machine-learning models to predict the risk of RTI/STI among young women in Delhi, India, by analysing self-reported symptoms along with demographic, reproductive health, lifestyle, and hygiene-related factors. The data were analysed using multiple machine learning algorithms, including decision trees, random forest, k-nearest neighbors (KNN), stochastic gradient descent (SGD), and XGBoost. Prior to analysis, the data was preprocessed through different steps including missing-value imputation, outlier removal using the interquartile range (IQR) method, and the synthetic minority oversampling technique (SMOTE) to address class imbalance.
RESULTS: A comparative analysis of model performance revealed that the XGBoost algorithm achieved the highest accuracy (89.1%) and the greatest area under receiver operating characteristics curve (ROC-AUC = 0.935), with random forest performing strongly as the second-best model (accuracy = 87.6%, ROC-AUC = 0.921), demonstrating its strong ability to capture non-linear relationships among RTI risk factors. SHAP (SHapley Additive exPlanations) analysis identified family medical history, personal RTI history, and menstrual cycle characteristics as the most influential predictors.
DISCUSSION: The findings underscore the potential of machine learning-driven predictive models in RTI risk stratification, screening support, and hypothesis generation.