Forecasting of Menstruation using a Calendar-based Method based on a Web Application

Abstract

Accurately predicting menstrual dates is valuable for women, yet it poses a significant challenge, particularly for those with irregular cycles. This study introduces a web application that utilises a calendar-based method to predict upcoming menstrual dates while offering additional features such as tracking past periods, logging symptoms, and providing personalised health advice. Using the software development life cycle approach, the critical contribution of this work is the development of a user-friendly tool that not only aids in menstrual tracking but also highlights the limitations of calendar-based predictions, especially for women with irregular cycles. Our findings suggest that while the method provides accurate predictions for women with regular cycles, it is less reliable for those with irregular cycles due to its dependence on a fixed cycle length range. This study underscores the need for more adaptive models to improve prediction accuracy for a broader population.