Machine Learning Model to Detect Mood Swings in Women during Menstrual Time – Review

Abstract

Machine learning is transforming mood detection by leveraging vast datasets to identify intricate patterns and correlations. One significant application of this technology is predicting mood variations during the menstrual cycle, providing valuable insights into the biological and psychological factors that influence emotional states. By integrating physiological data, behavioural indicators, and self-reported mood assessments, machine learning models can analyse hormonal fluctuations, sleep patterns, physical activity, and stress levels to develop personalized mood predictions. This data-driven approach enhances the understanding of how hormonal changes impact emotional well-being, enabling more precise mental health strategies. Unlike traditional methods, which rely solely on subjective reporting, machine learning offers objective and continuous monitoring, leading to early intervention and personalized recommendations. These insights can be particularly beneficial for individuals experiencing mood disorders such as Premenstrual Dysphoric Disorder (PMDD) or heightened emotional sensitivity during specific menstrual phases. By utilizing predictive analytics, machine learning contributes to adaptive mental health support, improving emotional resilience and well-being. This innovative integration of artificial intelligence in health monitoring fosters the development of personalized therapeutic strategies, helping individuals manage their moods more effectively throughout the menstrual cycle. Ultimately, machine learning-driven mood detection has the potential to revolutionize mental health care by offering proactive, data-driven solutions tailored to individual needs.