Integrating Machine Learning for PCOS Detection and femBot: A Novel NLP-Based Menstrual Health Chatbot for Women’s Health Support

An increase in the incidence of reproductive health disorders has made the need for innovative approaches in the area of timely diagnosis as well as health education imperative. One of the most prevalent forms of reproductive health disorder is Polycystic Ovary Syndrome (PCOS). PCOS often goes undiagnosed and has vague symptoms, making it a potential risk for the long-term adverse effects of infertility and metabolic disorders. It is unlikely that traditional methods of diagnosis will be accessible in underserved places. This paper discusses an integrated approach combining machine learning (ML) for PCOS detection and a natural language processing (NLP)-powered chatbot, femBot, for the awareness of menstrual health. Nine ML models were utilized, including random forest, support vector machines, and others, out of which XGBoost scored the highest accuracy of 96.33%. The femBot chatbot was developed using a sequential model that effectively responds to user queries in a test with an accuracy of 83.08%. These two technologies are accessible and provide accurate personalized health support so that healthcare outcomes can be extended to populations that have poor infrastructure. The proposed models show the power of ML and NLP combined to improve women's health care by facilitating early diagnosis and health education. The future directions will be the realization and implementation of both systems into a coherent health platform named FemWell for women's health support and education.