Machine Learning in Period, Fertility and Ovulation Tracking Application

Abstract

Machine learning has the potential to improve the accuracy of period tracking applications by analyzing patterns in menstrual cycle data. The ability to predict the timing of menstrual cycles is important for women's health, and can be used to provide personalized reminders and notifications, as well as to better understand and manage menstrual cycles. One of the main challenges in developing a Machine Learning algorithm for period tracking is the variability of menstrual cycles, which can be affected by a variety of factors such as stress, changes in weight, and certain medical conditions. One approach is to use time series forecasting algorithms, such as ARIMA and STL, which are designed to predict future values based on past data. These algorithms can be trained on historical menstrual cycle data to predict the timing of future cycles. Another approach is to use neural networks, which can model complex patterns in data and can be used to predict the next period date by analyzing patterns in menstrual cycle data, such as cycle length and symptoms. Random Forest and Gradient Boosting are ensemble methods used for classification and regression tasks, which can be used to predict the next period date by analyzing patterns in menstrual cycle data, such as cycle length and symptoms. Support Vector Machines (SVMs) are another Machine Learning algorithm that can be used for prediction, specifically in classification problems. In addition, the accuracy of predictions can be improved by including other relevant data such as information about stress levels, weight changes, and symptoms. Furthermore, by analyzing data from multiple users, machine learning can identify trends and patterns that may be useful in understanding and managing menstrual cycles more effectively. Overall, machine learning has the potential to greatly improve the accuracy and usefulness of period tracking applications, providing women with better tools for understanding and managing their menstrual cycles..