Early prediction of estrogen levels for post-hysterectomy menopausal syndrome using machine learning models with an Ayurveda approach- An observational study
DOI:
https://doi.org/10.70066/jahm.v13i6.1919Keywords:
Artificial intelligence (AI), Estrogen, Ayurveda, Shankhapushpi, ShatavariAbstract
Background: Ayurveda, an ancient Indian medical system, offers holistic healthcare solutions. The integration of Artificial intelligence (AI) with Ayurveda can enhance disease diagnosis, prakriti (body constitution) analysis, personalized treatment plans and drug discovery. Early deficiency of estrogen among women undergoing hysterectomy, lead to post hysterectomy menopausal syndrome (PHMS). Ayurveda medicines rich in phytoestrogens have been traditionally used for managing the menopausal symptoms.
Objectives: Early prediction of serum estrogen levels in PHMS using machine learning models.
Materials & Methods: A randomized controlled clinical trial (RCT) evaluated the efficacy of Shatavari (Asparagus racemosus) and Shankhapushpi (Convolvolus pluricaulis) powders over a period of 45 days, in managing PHMS assessed through symptom severity, hormonal changes and quality of life. Findings suggest possible efficacy of these herbs as non-hormonal medicines in managing PHMS, as serum estrogen levels (p<0.001) were significantly improved by Asparagus racemosus (A. racemosus) and stable estrogen levels (p = 0.334) were maintained by C. pluricaulis.
Further machine learning models are evaluated for predicting serum estrogen levels based on clinical and demographic data. By applying regression models such as Ridge Regression, Random Forest, XGBoost, KNN and SVR are trained and evaluated using MAE, MSE, SMSE and R² score.
Results: The results indicate that the Support Vector Regression (SVR) model achieves the highest accuracy (R² = 0.79), making it a promising tool for hormone level prediction.
Conclusion: This proposed work is focused on Ayurveda management perspectives for estrogen regulation in PHMS by early prediction of estrogen levels through machine learning models.
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