Dec 1, 2015 · We build an artificial neural network model to analyze menopausal problem. The parameters are optimized for higher precision performance.
Highlights · We build an artificial neural network model to analyze menopausal problem. · The parameters are optimized for higher precision performance.
We build an artificial neural network model to analyze menopausal problem.The parameters are optimized for higher precision performance.
This prediction model, based on nationwide survey data, can be used as a preliminary screening tool to identify post-menopausal women at high risk of stroke.
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Machine learning also has shown promising results diagnosing menopause symptoms. In thirty-one women with hot flashes, applying SVMs improved the performance ...
Machine Learning Approaches to Identify Factors Associated with ...
pmc.ncbi.nlm.nih.gov › PMC8093602
Apr 20, 2021 · The most important factors associated with VMS were age, menopause age, thyroid-stimulating hormone, and monocyte, triglyceride, gamma glutamyl transferase, ...
Oct 18, 2022 · The app provides insights, education and a broad range of therapeutic support for women throughout their menopause journey and leverages several ...
This prediction model, based on nationwide survey data, can be used as a pre- liminary screening tool to identify post- menopausal women at high risk of stroke.
Xian Li et al. (2015) [28] have focused on and proposed an artificial neural network model to predict menopausal symptoms and risk factors. Menopausal samples ...
Aim: The aim of this study is to develop and optimize artificial neural network models for accurate prediction of premature ovarian failure (POF), ...