Women’s Health Recommendation System using Large Language Models

Globally, numerous systems exist for tracking women’s health, recording menstrual cycles, and predicting
outcomes. However, most of these applications primarily focus on identifying symptoms and predicting the next menstrual cycle. Few solutions aim to enhance women’s overall physical health and immunity. To address this gap, we propose a Women’s Health Recommendation System designed to cater to women’s diverse health needs. This system leverages crowdsourced data and large language models (LLMs) to provide personalized dietary, exercise, and lifestyle recommendations tailored to the phases of the menstrual cycle. Polycystic Ovary Syndrome (PCOS), a leading cause of anovulation and infertility, serves as a critical focus. While there is no definitive cure for PCOS, symptoms can be managed through medication, lifestyle modifications, and fertility treatments, with prevention supported by maintaining a healthy lifestyle. The proposed system integrates a prediction model and an LLM. The prediction model classifies menstrual cycle phases to inform the generation of customized diet and exercise recommendations, while the LLM addresses common menstruation-related queries. This comprehensive solution empowers users with actionable insights and suggestions to enhance their overall health and well-being.