# Data Mining Techniques for Endometriosis Detection in a Data-Scarce Medical Dataset
## 2 Materials and Methods
### Dataset from Multiple Sample Types
(2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Can synthetic data be a proxy for real clinical trial data? A validation study. _BMJ Open_**2021**, _11_, e043497. [CrossRef] [PubMed]* (30) [PERSON]; [PERSON]; [PERSON] [PERSON] _Diseho de un Modelo Para Generar Datos Sinteticos en Investigacion Medica_; Universidad de Alcala: Alcala de Henares, Spain, 2023; Volume 12. * (31) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Analyzing Medical Research Results Based on Synthetic Data and Their Relation to Real Data Results: Systematic Comparison From Five Observational Studies. _JMIR Med. Inform._**2020**, \(8\), e16492. [CrossRef]
* (32) [PERSON] Data Mining and Knowledge Discovery Handbook. In _Data Mining and Knowledge Discovery Handbook_; Chapter Data Mining for Imbalanced Datasets: An Overview; Springer: New York, NY, USA, 2010; pp. 875-886. [CrossRef]
* (33) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Synthetic data generation: State of the art in health care domain. _Comput. Sci. Rev._**2023**, _48_, 100546. [CrossRef]
* (34) [PERSON]; [PERSON] Diagnosis of Endometrosis. _Semin. Reprod. Med._**2003**, _21_, 193-208. [CrossRef]
* (35) [PERSON]; [PERSON] [PERSON]; [PERSON] Invasive and non-invasive methods for the diagnosis of endometriosis. _Clin. Obstet. Gyncol._**2010**, _53_, 413-419. [CrossRef] [PubMed]
* (36) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Scikit-learn: Machine learning in Python. _[PERSON]. Learn. Res._**2011**, _12_, 2825-2830. * (37) [PERSON]; [PERSON]; [PERSON] Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification. _Remote Sens._**2019**, _11_, 185. [CrossRef]
* (38) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier]. _IEEE Comput. Intell. Mag._**2018**, _13_, 59-76. [CrossRef]
* (39) [PERSON]; [PERSON] Reliable Accuracy Estimates from k-Fold Cross Validation. _IEEE Trans. Knowl. Data Eng._**2020**, _32_, 1586-1594. [CrossRef]
* (40) [PERSON] Supervised Analysis When the Number of Candidate Features (p) Greatly Exceeds the Number of Cases (n). _SIGKDD Explor. Nessl._**2003**, \(5\), 31-36. [CrossRef]
* (41) [PERSON]; [PERSON] On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation. _J. Mach. Learn. Res._**2010**, _11_, 2079-2107. * (42) [PERSON]; [PERSON]; [PERSON]; [PERSON] GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems. _Appl. Soft Comput._**2014**, _17_, 23-31. [CrossRef]
* (43) [PERSON]; [PERSON]; [PERSON] An Introduction to Logistic Regression Analysis and Reporting. _J. Educ. Res._**2002**, _96_, 3-14. [CrossRef]
* (44) [PERSON] Induction of Decision Trees. _Mach. Learn._**1986**, \(1\), 81-106. [CrossRef]
* (45) [PERSON]; [PERSON]; [PERSON]; [PERSON] Dual unification of bi-class support vector machine formulations. _Pattern Recognit._**2006**, _39_, 1325-1332. [CrossRef]
* (46) [PERSON]; [PERSON]; [PERSON] SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance. _TELKOMNIKA (Telecommun. Comput. Electron. Control)_**2016**, _14_, 1502. [CrossRef]
* (47) [PERSON]; [PERSON]; [PERSON]; [PERSON] Categorizing paintings in art styles based on qualitative color descriptors, quantitative global features and machine learning (QArt-Learn). _Expert Syst. Appl._**2018**, _97_, 83-94. [CrossRef]

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.