Jamilynn Mackenzie Modelo, Mervis Encelan and Marilou B. Mangrobang, Pamantasan ng Lungsod ng Maynila, Philippines

Menstrual health plays a critical role in an individual’s overall well-being, influencing physical, emotional, and reproductive health. Tracking menstrual cycles provides valuable insights into hormonal patterns, allowing for early detection of disorders such as hormonal imbalances. However, most period tracking applications are designed for users with regular cycles, often resulting in inaccurate predictions for individuals with irregular menstruation or Polycystic Ovary Syndrome (PCOS). This study proposes the development of Evelune, a menstrual and PCOS management mobile application that integrates KMeans Clustering and a rule-based prediction system to improve menstrual phase prediction and health awareness. The K-Means algorithm organizes user input into groups based on similarities in symptom patterns, which are then analyzed through a rule-based system that predicts menstrual phases and provides personalized health insights. Evelune further implements data privacy features such as AES-256 encryption, Firebase Authentication, HTTPS communication, and Multi-Factor Authentication (MFA) to ensure user data protection. The system is evaluated using ISO/IEC 25010:2023 quality standards, emphasizing functional suitability, usability, reliability, and security. Evelune aims to empower Filipino users, particularly those managing PCOS, by promoting reproductive health literacy, accurate symptom tracking, and privacy-conscious self-care practices.

Menstrual Health, Polycystic Ovary Syndrome (PCOS), K-Means Clustering, Rule-Based System, Mobile Health Application, Data Application, Data Privacy, Reproductive Health Literacy