Age at natural menopause; A data mining approach (Data from the National Health and Nutrition Examination Survey 2013-2014)

Abstract

Background: The timing of the age at which menopause occurs varies among female populations. This variation is attributed to genetic and environmental factors. This study aims to investigate the determinants of early and late-onset menopause. Methods: We used data from the National Health and Nutrition Examination Survey 2013-2014 for 762 naturally menopause women. Data on sociodemographic, lifestyle, examination, and laboratory characteristics were examined. We used random forest (RF), support vector machine (SVM), and logistic regression (LR) to identify important determinants of early and late-onset menopause. We compared the performance of models using sensitivity, specificity, Brier score, and area under the receiver operating characteristic (AUROC). The top determinants were assessed by using the best performing models, using the mean decease in Gini. Results: Random forest outperformed LR and SVM with overall AUROC 99% for identifying related factors of early and late-onset menopause (Brier score: 0.051 for early and 0.005 for late-onset menopause). Vitamin B12 and age at menarche were strongly related to early menopause. Also, methylmalonic acid (MMA), vitamin D, body mass index (BMI) were among the top highly ranked factors contributing to early menopause. Features such as age at menarche, MMA, sex hormone-binding globulin (SHBG), BMI, vitamin B12 were the most important covariate for late-onset menopause. Conclusions: Menarche age and BMI are among the important contributors of early and late-onset menopause. More research on the association between vitamin D, vitamin B12, SHBG, and menopause timing is required which will produce invaluable information for better prediction of menopause timing.

Keywords

• data mining
• menopause
• nutrition surveys