Jupyter Notebooks from "Data mining techniques for endometriosis detection in a data-scarce medical dataset".
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This repository provides Jupyter notebooks that demonstrate the application of decision tree, logistic regression, and support vector machine classifiers for detecting endometriosis in a data-scarce medical dataset.
One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works
Abstract
The Jupyter notebooks presented in this repository are used to calculate the results, tables and figures in the journal paper: "Data mining techniques for endometriosis detection in a data-scarce medical dataset".Filesdecisiontree_classifier.ipynb: Jupyter Notebook for the classification using DecisionTree.log_regression_classifier.ipynb: Jupyter Notebook for the classification using LogisticRegression.svc_classifier.ipynb: Jupyter Notebook for the classification using svm SVC.microbiome_analysis.ipynb: Jupyter Notebook for initial analysis and chart distribution generation.
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- last seen: 2026-05-11T08:34:21.999627+00:00
License: CC0
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