Abstract

Menstrual health is a vital aspect of a woman’s overall well-being. It has been observed that menstrual cycles are often influenced by various lifestyle factors such as stress, sleep patterns, physical activities, and body mass index (BMI). Many women experience menstrual irregularities, including frequent or delayed cycles, which may impact overall health. Many menstrual tracking systems have been proposed, but they primarily focus on recording cycle dates and lack predictive analysis and personalized health recommendations. This work proposes a web based menstrual health prediction and alert system using machine learning to support effective management of mensural related issues. Furthermore, this system enables users to input historical menstrual cycle data along with lifestyle related information. This data is analyzed using a Linear Regression model to predict the next expected menstrual cycle date. Data preprocessing techniques are also employed to improve the reliability and accuracy of predictions. The predicted results are presented through a user friendly web interface developed using Flask, ensuring ease of interaction. Additionally, an alert mechanism is incorporated to notify users when the predicted menstrual date approaches or occurs. This enables users to prepare in advance and better understand their cycle patterns. The proposed system also focuses on identifying irregular patterns to enhance user awareness, while explicitly avoiding any form of medical diagnosis. Our results demonstrate that the proposed system effectively promotes menstrual health awareness through a user friendly, smart and predictive platform.