Predictive Modeling of Menstrual Cycle Length: A Time Series Forecasting Approach
This work addresses menstrual cycle prediction for women's health and planning, but it is incremental as it applies existing methods to this domain.
The study tackled predicting menstrual cycle length using machine learning time series forecasting methods, including ARIMA, Huber Regression, Lasso Regression, Orthogonal Matching Pursuit, and LSTM, and found it possible to accurately predict onset and duration.
A proper forecast of the menstrual cycle is meaningful for women's health, as it allows individuals to take preventive actions to minimize cycle-associated discomforts. In addition, precise prediction can be useful for planning important events in a woman's life, such as family planning. In this work, we explored the use of machine learning techniques to predict regular and irregular menstrual cycles. We implemented some time series forecasting algorithm approaches, such as AutoRegressive Integrated Moving Average, Huber Regression, Lasso Regression, Orthogonal Matching Pursuit, and Long Short-Term Memory Network. Moreover, we generated synthetic data to achieve our purposes. The results showed that it is possible to accurately predict the onset and duration of menstrual cycles using machine learning techniques.