AI-Assisted Menopause Management System: Utilizing Technology to Improve Quality of Life for Ageing Women

Authors

Oluwatoyin Mary Yerokun

Department of Computer Science, Faculty of Science, Ekiti State University, Ado-Ekiti (Nigeria)

Nigeria Dipo Theophilus Akomolafe

Department of Computer Science, School of Computing, Olusegun Agagu University of Science and Technology, Okitipupa (Nigeria)

Isoken Praise Ehigie

Department of Computer Science, College of Science and Computing, Wellspring University, Benin City (Nigeria)

Article Information

DOI: 10.51244/IJRSI.2026.1306000038

Subject Category: Computer Science

Volume/Issue: 13/6 | Page No: 646-654

Publication Timeline

Submitted: 2026-05-22

Accepted: 2026-05-27

Published: 2026-06-18

Abstract

AI came into existence to improve the quality of human living from birth to death, including and especially transition stages such as reproductive-ageing years in women, known as menopause. Often accompanied by debilitating symptoms that impact daily life, menopause is currently managed by religious, cultural and social systems which puts career women at a disadvantage as they are branded sickly or incapable of full delivery of services. The few machines created to relieve the menopause strains on women are at best, one-size-fits-all wearables. This research investigated the neglected individual differences approach in symptom profiles and health needs and employed artificial intelligence tools to design and evaluate an intelligent menopause management system that can provide personalized support and guidance to women in navigating the menopause phases. Data collected through surveys and interviews showed that quality of life of women drastically reduced from perimenopause years and often, women are forced to retire prematurely. The system designed analyzed collected data, identified patterns, predicted symptoms severity, immediate relief actions and potential complications with correlations between symptoms. It provides a personalized approach to treatment options and communication tools with healthcare providers.

Keywords

analytics, menopause, machine, predictive

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References

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