A new model for predicting the occurrence of polycystic ovary syndrome: Based on data of tongue and pulse.

ABSTRACT:

Background and objective

Polycystic ovary syndrome is one of the most common types of endocrine and metabolic diseases in women of reproductive age that needs to be screened early and assessed non-invasively. The objective of the current study was to develop prediction models for polycystic ovary syndrome based on data of tongue and pulse using machine learning techniques.

Methods

A dataset of 285 polycystic ovary syndrome patients and 201 healthy women were investigated to identify the significant tongue and pulse parameters for predicting polycystic ovary syndrome. In this study, feature selection was performed using least absolute shrinkage and selection operator regression. Several machine learning algorithms (multilayer perceptron classifier, eXtreme gradient boosting c

SUBMITTER: Wang W 

PROVIDER: S-EPMC10281487 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature