Research Article: Establishment and Analysis of an Artificial Neural Network Model for Early Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques
Abstract:
The Polycystic Ovary Syndrome (PCOS) is an endocrine disorder characterized by heterogeneity and closely linked to various symptoms. The National Institutes of Health (NIH), the European Society for Human Reproduction and Embryology (ESHRE) and the American Society for Reproductive Medicine (ASRM) with their consensus,, and the Androgen Excess Society (AES) with its reference criteria are the three main bodies proposing diagnostic criteria for PCOS. However, despite the proposals for these standards, a consensus has yet to be reached within the field. The complex genetic architecture forms the basis for the multifactorial etiology of PCOS. Moreover, previous studies have found that race is closely associated with PCOS phenotype due to different genetic metabolic disorders and environmental tendencies. Therefore, the aim of the study is to investigate unique and essential gene combinations while developing an early diagnostic model for PCOS.
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