Age at natural menopause and its determinants in female population of Kharameh cohort study: Comparison of regression, conditional tree and forests

• Published in: PloS one Vol. 19; no. 4; p. e0300448
• Main Authors: Pasokh, Zahra, Seif, Mozhgan, Ghaem, Haleh, Rezaianzadeh, Abbas, Ghoddusi Johari, Masoumeh
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.04.2024; Public Library of Science (PLoS)
• Subjects: Age Factors; Algorithms; Biology and Life Sciences; Breast feeding; Cohort Studies; Comparative analysis; Cross-Sectional Studies; Data mining; Ecology and Environmental Sciences; Female; Forests and forestry; Health aspects; Humans; Iran; Machine learning; Medicine and Health Sciences; Menopause; Menopause - physiology; Methods; People and Places; Physiological aspects; Postmenopausal women; Psychological aspects; Quality of Life; Target marketing; Type 2 diabetes; Women; Iran
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0300448

Natural menopause is defined as the permanent cessation of menstruation that occurs after 12 consecutive months of amenorrhea without any obvious pathological or physiological cause. The age of this phenomenon has been reported to be associated with several health outcomes. This study aimed to estimate the Age at Natural Menopause (ANM) and to identify reproductive and demographic factors affecting ANM. This cross-sectional, population-based study was conducted on 2517 post-menopausal women aged 40-70 years participating in the first phase of the PERSIAN cohort study of Kharameh, Iran, during 2014-2017. To more accurately detect the determinants of ANM, we applied multiple linear regression beside some machine learning algorithms including conditional tree, conditional forest, and random forest. Then, the fitness of these methods was compared using Mean Squared Error (MSE) and Pearson correlation coefficient. The mean±SD of ANM was 48.95±6.13. Both applied forests provided more accurate results and identified more predictors. However, according to the final comparison, the conditional forest was the most accurate method which recognized that more pregnancies, longer breastfeeding, Fars ethnicity, and urbanization have the greatest impact on later ANM. This study found a wide range of reproductive and demographic factors affecting ANM. Considering our findings in decision-making can reduce the complications related to this phenomenon and, consequently, improve the quality of life of post-menopausal women.