Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/54281
Title: Application of machine learning tools for evaluating the impact of premenopausal hysterectomy on serum anti-mullerian hormone levels
Authors: Sreenu, Boddupally
Kameswari, SV
Naushad, Shaik Mohammad
Kutala, Vijay Kumar
Keywords: Anti-Mullerian Hormone (AMH);Hysterectomy;Pre-menopause;Ovarian function;Oxidative stress
Issue Date: Apr-2020
Publisher: NISCAIR-CSIR, India
Abstract: Women who have had premenopausal total hysterectomy at a young age could probably experience partial or total loss of ovarian function. The purpose of this retrospective cross-sectional study is to investigate the ovarian function in women underwent hysterectomy at an early age. A total of 1165 subjects comprised of 685 hysterectomised women and 480 age matched controls were enrolled in the study. We found that there is a steady decline in serum Anti Mullerian Hormone (AMH) levels, a marker of ovarian function after every five years post - hysterectomy in early age groups (20-30 yr and 31-40 yr) followed by loss of ovarian function in the age group of 40-50 yr. The application of multiple linear regression and machine learning tools has revealed that AMH is positively correlated with LH and estradiol and negatively correlated with age, FSH, years since hysterectomy and vitamin D. Serum AMH level of <0.08 ng/ml is associated with the increased of FSH, decreased LH and estradiol. The decreased ovarian function is associated with lower calcium levels, which are likely to influence the bone health. In conclusion, by utilizing multiple linear regression and machine learning tools, we found that serum FSH is the most important in predicting the AMH-mediated ovarian function.
Page(s): 245-251
URI: http://nopr.niscair.res.in/handle/123456789/54281
ISSN: 0975-0959 (Online); 0301-1208 (Print)
Appears in Collections:IJBB Vol.57(2) [April 2020]

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