PYTHON

Polycystic Ovary Syndrome (PCOS) is a chronic endocrine disorder requiring continuous monitoring to manage its complex and irregular symptoms. Current digital health applications often fail to address this need, providing generalized tracking, inadequate visualization, and insufficient privacy protection. This project presents the design and development of a specialized, Python-based symptom tracking and visualization system to empower women with PCOS. Built using an Object- Oriented Analysis and Design (OOAD) methodology, the system ensures a modular and user- centered architecture. It leverages Matplotlib for generating insightful visualizations of symptoms, mood, and lifestyle factors. A Flask-based web interface offers an intuitive dashboard for data entry and review, with a commitment to user privacy through local data storage. The resulting system demonstrates that open-source Python technologies can effectively bridge gaps in women's digital health by providing an affordable, transparent, and adaptable solution. It facilitates improved health literacy, data-driven self-management, and enhances patient-clinician communication, highlighting the significant potential of Python in advancing personalized chronic disease management.