Optimizing Menstruation Symptom Management: A Machine Learning Approach to Personalized Nutrition Recommendations

This research provides through method for building a recommendation system for health concerns for women the significant number of individuals’ day-to-day activities are affected by menstruation symptoms including pains, mood swings and fatigue. Be it making an individual food prescription or eradicating these symptoms, nutrition is an absolute factor. Thus, according to this work, there is a concept of a method that based on machine learning will help predict menstrual symptoms and provide tailored nutrition recommendations. The analysis of the respondents scores for six menstrual cycles revealed that magnesium, omega-3 fatty acids, vitamin D, and calcium are significant nutrients affecting the symptom severity level. Various Machine Learning models to compared include the Neural Networks, Support Vector Machines and Random Forests. Out of all the models the highest was achieved by the Random Forest model for the targeted recommendations of the proper nutrition adjusted to the person, where the prediction accuracy was 98%. The findings of the provided results support the view that is hope and the intensity of menstruation symptoms reduced significantly.