Predictive Modeling of Dysmenorrhea Onset and Severity in Adolescent Girls

Dysmenorrhea, commonly known as period pain, significantly affects the quality of life for adolescent girls and post-marriage women, yet there are limited tools available for accurately predicting its onset and severity. The issue addressed by this research is the lack of effective prediction methods for dysmenorrhea. The outcome of the study is the development of a machine learning-based predictive model that utilizes predictive analytics and Generative Adversarial Networks (GANs) for period pain prediction. The model was trained using neural networks and enhanced with data augmentation to improve dataset diversity and robustness. By analyzing comprehensive data that includes physiological, psychological, and lifestyle factors, the model achieved superior accuracy in predicting the onset and severity of dysmenorrhea, compared to traditional methods. This research demonstrates the potential of machine learning to offer personalized healthcare interventions and early detection for dysmenorrhea. It also highlights the challenges faced by post-marriage women with dysmenorrhea, advocating for targeted healthcare solutions across life stages. The study showcases the power of advanced predictive analytics to transform the management of menstrual health and improve adolescent health outcomes.