AI-Driven Impact Analysis of Clinical Parameters Influencing Menopause

Abstract

Menopause, a critical transition in a woman's life, is associated with diverse symptoms. Most existing studies focus on the analysis of menopause severity based on a single or specific factor only. Interrelationship study between the influencing factors and severity of menopause remains under explored. This study explores the impact of various clinical parameters on menopause severity using a data-driven approach to improve clinical understanding, interpretability and management. By analyzing key clinical parametersincluding symptoms, hormonal levels, diagnostic, demographic parameters-this research identifies the most contributing features that influence the severity of menopause. The workflow involves data preprocessing, feature selection and validation. Initially the data is cleaned, and data imbalance is addressed using Synthetic Minority Over-sampling Technique. To facilitate the identification of key factors, feature selection techniques are integrated with machine learning techniques like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest, and XGBoost. Model evaluation and hyperparameter tuning help to make reliable identification of the impactful features. The identified clinical features includeSystolic Blood Pressure, Estrogen, Free Triiodothyronine, Triglycerides, Free Thyroxine, Progesterone, Age, Follicle Stimulating Hormone, Prolactin and Body Mass Index whose significance was validated through statistical analyses and clinical evidence. This approach serves as a clinical decision support system aiding tailored treatment and management of menopause severity.

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