Developing Explainable AI Models to Identify Perimenopause Symptoms

Abstract

Perimenopausal transition is a natural phase in every woman's life that is often marked by a range of physical and psychological symptoms that can remarkably impact the quality of life. The gradual decline in oestrogen levels leads to a wide array of symptoms, including mood swings, hot flashes, sleep disturbances, and irregular menstrual cycles. Managing perimenopausal symptoms effectively requires accurate detection and timely intervention to alleviate discomfort and optimise quality of life. AI-powered early identification and management of these symptoms are crucial for women's health and well-being. Machine learning techniques with the use of large datasets, self-reported symptoms, and clinical records offer a powerful tool for analysing complex patterns to achieve high accuracy and reliability in symptom recognition. AI-driven devices can aid in symptom tracking, personalised applications, remote monitoring, predictive analytics, and treatment efficacy assessment, thus improving clinical decision-making, patient outcomes, and the overall quality of women's health.