1
|
Verma P, Maan P, Gautam R, Arora T. Unveiling the Role of Artificial Intelligence (AI) in Polycystic Ovary Syndrome (PCOS) Diagnosis: A Comprehensive Review. Reprod Sci 2024:10.1007/s43032-024-01615-7. [PMID: 38907128 DOI: 10.1007/s43032-024-01615-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 06/02/2024] [Indexed: 06/23/2024]
Abstract
Polycystic Ovary Syndrome (PCOS) is one of the most widespread endocrine and metabolic disorders affecting women of reproductive age. Major symptoms include hyperandrogenism, polycystic ovary, irregular menstruation cycle, excessive hair growth, etc., which sometimes may lead to more severe complications like infertility, pregnancy complications and other co-morbidities such as diabetes, hypertension, sleep apnea, etc. Early detection and effective management of PCOS are essential to enhance patients' quality of life and reduce the chances of associated health complications. Artificial intelligence (AI) techniques have recently emerged as a popular methodology in the healthcare industry for diagnosing and managing complex diseases such as PCOS. AI utilizes machine learning algorithms to analyze ultrasound images and anthropometric and biochemical test result data to diagnose PCOS quickly and accurately. AI can assist in integrating different data sources, such as patient histories, lab findings, and medical records, to present a clear and complete picture of an individual's health. This information can help the physician make more informed and efficient diagnostic decisions. This review article provides a comprehensive analysis of the evolving role of AI in various aspects of the management of PCOS, with a major focus on AI-based diagnosis tools.
Collapse
|
2
|
Zad Z, Jiang VS, Wolf AT, Wang T, Cheng JJ, Paschalidis IC, Mahalingaiah S. Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records. Front Endocrinol (Lausanne) 2024; 15:1298628. [PMID: 38356959 PMCID: PMC10866556 DOI: 10.3389/fendo.2024.1298628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 01/08/2024] [Indexed: 02/16/2024] Open
Abstract
Introduction Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis. Methods This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016. The study population included 30,601 women aged 18-45 years without concurrent endocrinopathy who had any visit to Boston Medical Center for primary care, obstetrics and gynecology, endocrinology, family medicine, or general internal medicine. Four prediction outcomes were assessed for PCOS. The first outcome was PCOS ICD-9 diagnosis with additional model outcomes of algorithm-defined PCOS. The latter was based on Rotterdam criteria and merging laboratory values, radiographic imaging, and ICD data from the EHR to define irregular menstruation, hyperandrogenism, and polycystic ovarian morphology on ultrasound. Results We developed predictive models using four machine learning methods: logistic regression, supported vector machine, gradient boosted trees, and random forests. Hormone values (follicle-stimulating hormone, luteinizing hormone, estradiol, and sex hormone binding globulin) were combined to create a multilayer perceptron score using a neural network classifier. Prediction of PCOS prior to clinical diagnosis in an out-of-sample test set of patients achieved an average AUC of 85%, 81%, 80%, and 82%, respectively in Models I, II, III and IV. Significant positive predictors of PCOS diagnosis across models included hormone levels and obesity; negative predictors included gravidity and positive bHCG. Conclusion Machine learning algorithms were used to predict PCOS based on a large at-risk population. This approach may guide early detection of PCOS within EHR-interfaced populations to facilitate counseling and interventions that may reduce long-term health consequences. Our model illustrates the potential benefits of an artificial intelligence-enabled provider assistance tool that can be integrated into the EHR to reduce delays in diagnosis. However, model validation in other hospital-based populations is necessary.
Collapse
Affiliation(s)
- Zahra Zad
- Division of Systems Engineering, Center for Information and Systems Engineering (CISE), Boston University, Brookline, MA, United States
| | - Victoria S. Jiang
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, United States
| | - Amber T. Wolf
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Taiyao Wang
- Division of Systems Engineering, Center for Information and Systems Engineering (CISE), Boston University, Brookline, MA, United States
| | - J. Jojo Cheng
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, United States
| | - Ioannis Ch. Paschalidis
- Division of Systems Engineering, Center for Information and Systems Engineering (CISE), Boston University, Brookline, MA, United States
- Department of Electrical & Computer Engineering, Department of Biomedical Engineering, and Faculty for Computing & Data Sciences, Boston University, Boston, MA, United States
| | - Shruthi Mahalingaiah
- Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, United States
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| |
Collapse
|
3
|
Wu Y, Xiao Q, Wang S, Xu H, Fang Y. Establishment and Analysis of an Artificial Neural Network Model for Early Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques. J Inflamm Res 2023; 16:5667-5676. [PMID: 38050562 PMCID: PMC10693771 DOI: 10.2147/jir.s438838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/10/2023] [Indexed: 12/06/2023] Open
Abstract
Background 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.
Collapse
Affiliation(s)
- Yumi Wu
- Institute of Acupuncture and Moxibustion of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
| | - QiWei Xiao
- Institute of Acupuncture and Moxibustion of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
| | - ShouDong Wang
- The Out-Patient Department of TCM of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
| | - Huanfang Xu
- Institute of Acupuncture and Moxibustion of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
- Acupuncture and Moxibustion Hospital of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
| | - YiGong Fang
- Institute of Acupuncture and Moxibustion of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
- Acupuncture and Moxibustion Hospital of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China
| |
Collapse
|
4
|
Lim J, Li J, Feng X, Feng L, Xia Y, Xiao X, Wang Y, Xu Z. Machine learning classification of polycystic ovary syndrome based on radial pulse wave analysis. BMC Complement Med Ther 2023; 23:409. [PMID: 37957660 PMCID: PMC10644435 DOI: 10.1186/s12906-023-04249-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 11/07/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Patients with Polycystic ovary syndrome (PCOS) experienced endocrine disorders that may present vascular function changes. This study aimed to classify and predict PCOS by radial pulse wave parameters using machine learning (ML) methods and to provide evidence for objectifying pulse diagnosis in traditional Chinese medicine (TCM). METHODS A case-control study with 459 subjects divided into a PCOS group and a healthy (non-PCOS) group. The pulse wave parameters were measured and analyzed between the two groups. Seven supervised ML classification models were applied, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Random Forest, Logistic Regression, Voting, and Long Short Term Memory networks (LSTM). Parameters that were significantly different were selected as input features and stratified k-fold cross-validations training was applied to the models. RESULTS There were 316 subjects in the PCOS group and 143 subjects in the healthy group. Compared to the healthy group, the pulse wave parameters h3/h1 and w/t from both left and right sides were increased while h4, t4, t, As, h4/h1 from both sides and right t1 were decreased in the PCOS group (P < 0.01). Among the ML models evaluated, both the Voting and LSTM with ensemble learning capabilities, demonstrated competitive performance. These models achieved the highest results across all evaluation metrics. Specifically, they both attained a testing accuracy of 72.174% and an F1 score of 0.818, their respective AUC values were 0.715 for the Voting and 0.722 for the LSTM. CONCLUSION Radial pulse wave signal could identify most PCOS patients accurately (with a good F1 score) and is valuable for early detection and monitoring of PCOS with acceptable overall accuracy. This technique can stimulate the development of individualized PCOS risk assessment using mobile detection technology, furthermore, gives physicians an intuitive understanding of the objective pulse diagnosis of TCM. TRIAL REGISTRATION Not applicable.
Collapse
Affiliation(s)
- Jiekee Lim
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Jieyun Li
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Xiao Feng
- The First Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Guangzhou, 510405, P. R. China
| | - Lu Feng
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Yumo Xia
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Xinang Xiao
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
| | - Yiqin Wang
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China
- Shanghai Key Laboratory of Health Identification and Assessment, Shanghai, 201203, P. R. China
| | - Zhaoxia Xu
- School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, P. R. China.
- Shanghai Key Laboratory of Health Identification and Assessment, Shanghai, 201203, P. R. China.
| |
Collapse
|
5
|
Barrera FJ, Brown ED, Rojo A, Obeso J, Plata H, Lincango EP, Terry N, Rodríguez-Gutiérrez R, Hall JE, Shekhar S. Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review. Front Endocrinol (Lausanne) 2023; 14:1106625. [PMID: 37790605 PMCID: PMC10542899 DOI: 10.3389/fendo.2023.1106625] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 08/04/2023] [Indexed: 10/05/2023] Open
Abstract
Introduction Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS. Methods We applied a search strategy using the following databases MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Web of Science, and the IEEE Xplore Digital Library using relevant keywords. Eligible studies were identified, and results were extracted for their synthesis from inception until January 1, 2022. Results 135 studies were screened and ultimately, 31 studies were included in this study. Data sources used by the AI/ML interventions included clinical data, electronic health records, and genetic and proteomic data. Ten studies (32%) employed standardized criteria (NIH, Rotterdam, or Revised International PCOS classification), while 17 (55%) used clinical information with/without imaging. The most common AI techniques employed were support vector machine (42% studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML. Receiver operating curves (ROC) were employed to compare AI/ML with clinical diagnosis. Area under the ROC ranged from 73% to 100% (n=7 studies), diagnostic accuracy from 89% to 100% (n=4 studies), sensitivity from 41% to 100% (n=10 studies), specificity from 75% to 100% (n=10 studies), positive predictive value (PPV) from 68% to 95% (n=4 studies), and negative predictive value (NPV) from 94% to 99% (n=2 studies). Conclusion Artificial intelligence and machine learning provide a high diagnostic and classification performance in detecting PCOS, thereby providing an avenue for early diagnosis of this disorder. However, AI-based studies should use standardized PCOS diagnostic criteria to enhance the clinical applicability of AI/ML in PCOS and improve adherence to methodological and reporting guidelines for maximum diagnostic utility. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42022295287.
Collapse
Affiliation(s)
- Francisco J. Barrera
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, United States
- Plataforma INVEST Medicina, Universidad Autónoma de Nuevo León- Knowledge Education Research (UANL-KER), Unit Mayo Clinic (KER Unit Mexico), Universidad Autónoma de Nuevo León, Monterrey, Mexico
| | - Ethan D.L. Brown
- Reproductive Physiology and Pathophysiology Group, Clinical Research Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States
| | - Amanda Rojo
- Plataforma INVEST Medicina, Universidad Autónoma de Nuevo León- Knowledge Education Research (UANL-KER), Unit Mayo Clinic (KER Unit Mexico), Universidad Autónoma de Nuevo León, Monterrey, Mexico
| | - Javier Obeso
- Plataforma INVEST Medicina, Universidad Autónoma de Nuevo León- Knowledge Education Research (UANL-KER), Unit Mayo Clinic (KER Unit Mexico), Universidad Autónoma de Nuevo León, Monterrey, Mexico
| | - Hiram Plata
- Plataforma INVEST Medicina, Universidad Autónoma de Nuevo León- Knowledge Education Research (UANL-KER), Unit Mayo Clinic (KER Unit Mexico), Universidad Autónoma de Nuevo León, Monterrey, Mexico
| | - Eddy P. Lincango
- Knowledge and Evaluation Research Unit-Endocrinology (KER-Endo), Mayo Clinic, Rochester, MN, United States
| | - Nancy Terry
- Division of Library Services, Office of Research Services, National Institutes of Health, Bethesda, MD, United States
| | - René Rodríguez-Gutiérrez
- Plataforma INVEST Medicina, Universidad Autónoma de Nuevo León- Knowledge Education Research (UANL-KER), Unit Mayo Clinic (KER Unit Mexico), Universidad Autónoma de Nuevo León, Monterrey, Mexico
- Knowledge and Evaluation Research Unit-Endocrinology (KER-Endo), Mayo Clinic, Rochester, MN, United States
- Endocrinology Division, Department of Internal Medicine, University Hospital “Dr. José E. González”, Universidad Autonoma de Nuevo Leon, Monterrey, Mexico
| | - Janet E. Hall
- Reproductive Physiology and Pathophysiology Group, Clinical Research Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States
| | - Skand Shekhar
- Reproductive Physiology and Pathophysiology Group, Clinical Research Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States
| |
Collapse
|
6
|
A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS). APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2023. [DOI: 10.1155/2023/9686697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023] Open
Abstract
One of the leading causes of female infertility is PCOS, which is a hormonal disorder affecting women of childbearing age. The common symptoms of PCOS include increased acne, irregular period, increase in body hair, and overweight. Early diagnosis of PCOS is essential to manage the symptoms and reduce the associated health risks. Nonetheless, the diagnosis is based on Rotterdam criteria, including a high level of androgen hormones, ovulation failure, and polycystic ovaries on the ultrasound image (PCOM). At present, doctors and radiologists manually perform PCOM detection using ovary ultrasound by counting the number of follicles and determining their volume in the ovaries, which is one of the challenging PCOS diagnostic criteria. Moreover, such physicians require more tests and checks for biochemical/clinical signs in addition to the patient’s symptoms in order to decide the PCOS diagnosis. Furthermore, clinicians do not utilize a single diagnostic test or specific method to examine patients. This paper introduces the data set that includes the ultrasound image of the ovary with clinical data related to the patient that has been classified as PCOS and non-PCOS. Next, we proposed a deep learning model that can diagnose the PCOM based on the ultrasound image, which achieved 84.81% accuracy using the Inception model. Then, we proposed a fusion model that includes the ultrasound image with clinical data to diagnose the patient if they have PCOS or not. The best model that has been developed achieved 82.46% accuracy by extracting the image features using MobileNet architecture and combine with clinical features.
Collapse
|
7
|
Fruh V, Cheng JJ, Aschengrau A, Mahalingaiah S, Lane KJ. Fine particulate matter and polycystic ovarian morphology. Environ Health 2022; 21:26. [PMID: 35180862 PMCID: PMC8855564 DOI: 10.1186/s12940-022-00835-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 01/19/2022] [Indexed: 05/02/2023]
Abstract
BACKGROUND Polycystic ovary morphology (PCOM) is an ultrasonographic finding that can be present in women with ovulatory disorder and oligomenorrhea due to hypothalamic, pituitary, and ovarian dysfunction. While air pollution has emerged as a possible disrupter of hormone homeostasis, limited research has been conducted on the association between air pollution and PCOM. METHODS We conducted a longitudinal cohort study using electronic medical records data of 5,492 women with normal ovaries at the first ultrasound that underwent a repeated pelvic ultrasound examination during the study period (2004-2016) at Boston Medical Center. Machine learning text algorithms classified PCOM by ultrasound. We used geocoded home address to determine the ambient annual average PM2.5 exposures and categorized into tertiles of exposure. We used Cox Proportional Hazards models on complete data (n = 3,994), adjusting for covariates, and additionally stratified by race/ethnicity and body mass index (BMI). RESULTS Cumulative exposure to PM2.5 during the study ranged from 4.9 to 17.5 µg/m3 (mean = 10.0 μg/m3). On average, women were 31 years old and 58% were Black/African American. Hazard ratios and 95% confidence intervals (CI) comparing the second and third PM2.5 exposure tertile vs. the reference tertile were 1.12 (0.88, 1.43) and 0.89 (0.62, 1.28), respectively. No appreciable differences were observed across race/ethnicity. Among women with BMI ≥ 30 kg/m2, we observed weak inverse associations with PCOM for the second (HR: 0.93, 95% CI: 0.66, 1.33) and third tertiles (HR: 0.89, 95% CI: 0.50, 1.57). CONCLUSIONS In this study of reproductive-aged women, we observed little association between PM2.5 concentrations and PCOM incidence. No dose response relationships were observed nor were estimates appreciably different across race/ethnicity within this clinically sourced cohort.
Collapse
Affiliation(s)
- Victoria Fruh
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Jay Jojo Cheng
- Department of Biostatistics and Medical Informatics, University of Wisconsin, 702 West Johnson Street, Madison, WI, USA
| | - Ann Aschengrau
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Shruthi Mahalingaiah
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Obstetrics and Gynecology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114-2696, USA
| | - Kevin J Lane
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA
| |
Collapse
|
8
|
Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network. BIOMED RESEARCH INTERNATIONAL 2020; 2020:2613091. [PMID: 32884937 PMCID: PMC7455828 DOI: 10.1155/2020/2613091] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/27/2020] [Accepted: 08/03/2020] [Indexed: 12/14/2022]
Abstract
Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathies. However, few studies have tried to develop a diagnostic model based on gene biomarkers. In this study, we applied a computational method by combining two machine learning algorithms, including random forest (RF) and artificial neural network (ANN), to identify gene biomarkers and construct diagnostic model. We collected gene expression data from Gene Expression Omnibus (GEO) database containing 76 PCOS samples and 57 normal samples; five datasets were utilized, including one dataset for screening differentially expressed genes (DEGs), two training datasets, and two validation datasets. Firstly, based on RF, 12 key genes in 264 DEGs were identified to be vital for classification of PCOS and normal samples. Moreover, the weights of these key genes were calculated using ANN with microarray and RNA-seq training dataset, respectively. Furthermore, the diagnostic models for two types of datasets were developed and named neuralPCOS. Finally, two validation datasets were used to test and compare the performance of neuralPCOS with other two set of marker genes by area under curve (AUC). Our model achieved an AUC of 0.7273 in microarray dataset, and 0.6488 in RNA-seq dataset. To conclude, we uncovered gene biomarkers and developed a novel diagnostic model of PCOS, which would be helpful for diagnosis.
Collapse
|