Analysis and Interpretation of Physiological Social Demographic Parameter in Menopausal Women

Abstract

A woman's menopause is normal period of her life. Some females have both emotional and physical symptoms throughout menopause. Depression is one of the many difficulties that some women experience after menopause. This project aims to enhance menopausal women's quality of life by predicting the onset of depression and tackling the lack of experts, knowledge, and awareness in this field. An interesting and difficult area of artificial intelligence study is the use of machine learning to forecast when postmenopausal women may have depressive symptoms. Through the use of supervised machine learning, this study develops a system that is remarkably accurate. Return on investment (ROI), area under the curve (AUC), recall, specificity, accuracy, F-Measure, and ROC are some of the metrics used by different classification methods to evaluate classifier performance. Random Forest and DT had the greatest accuracy rate of 99.04% among the classifiers we tested. In addition to predicting a patient's likelihood of having depression, this study analyzes the menopause according to its four stages: pre-, peri, meno-, and post-menopause. As a result, ML could be used to diagnose postmenopausal women with depression for the first time.