Journal of Inflammation Research (Nov 2023)
Establishment and Analysis of an Artificial Neural Network Model for Early Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques
Abstract
Yumi Wu,1 QiWei Xiao,1 ShouDong Wang,2 Huanfang Xu,1,3 YiGong Fang1,3 1Institute of Acupuncture and Moxibustion of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China; 2The Out-Patient Department of TCM of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China; 3Acupuncture and Moxibustion Hospital of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of ChinaCorrespondence: YiGong Fang; Huanfang Xu, Institute of Acupuncture and Moxibustion of China Academy of Chinese Medical Sciences, 16 Dongzhimennei South St, Dongcheng, Beijing, 100700, People’s Republic of China, Tel +86 13520175177, Fax +86 010-64089219, Email [email protected]; [email protected]: To identify novel gene combinations and to develop an early diagnostic model for Polycystic Ovary Syndrome (PCOS) through the integration of artificial neural networks (ANN) and random forest (RF) methods.Methods: We retrieved and processed gene expression datasets for PCOS from the Gene Expression Omnibus (GEO) database. Differential expression analysis of genes (DEGs) within the training set was performed using the “limma” R package. Enrichment analyses on DEGs using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), and immune cell infiltration. The identification of critical genes from DEGs was then performed using random forests, followed by the developing of new diagnostic models for PCOS using artificial neural networks.Results: We identified 130 up-regulated genes and 132 down-regulated genes in PCOS compared to normal samples. Gene Ontology analysis revealed significant enrichment in myofibrils and highlighted crucial biological functions related to myofilament sliding, myofibril, and actin-binding. Compared with normal tissues, the types of immune cells expressed in PCOS samples are different. A random forest algorithm identified 10 significant genes proposed as potential PCOS-specific biomarkers. Using these genes, an artificial neural network diagnostic model accurately distinguished PCOS from normal samples. The diagnostic model underwent validation using the independent validation set, and the resulting area under the receiver operating characteristic curve (AUC) values was consistent with the anticipated outcomes.Conclusion: Utilizing unique gene combinations, this research created a diagnostic model by merging random forest techniques with artificial neural networks. The AUC indicated a notably superior performance of the diagnostic model.Keywords: polycystic ovary syndrome, machine learning techniques, artificial neural network model, early diagnostic model, artificial neural networks, random forest
