{"paper_id":"de2f588c-0750-40ad-8040-9564f29ff0d4","body_text":"1\nVol.:(0123456789)Scientific Reports |        (2024) 14:10443  | https://doi.org/10.1038/s41598-024-61280-3\nwww.nature.com/scientificreports\nAuthor Correction: Self‑report \nsymptom‑based endometriosis \nprediction using machine learning\nAnat Goldstein  & Shani Cohen \nCorrection to: Scientific Reports https:// doi. org/ 10. 1038/ s41598- 023- 32761-8, published online 04 April 2023\nThe original version of this Article contained an error in the interpretation of the heatmap presented as Figure 1.\nAs a result, in the Results section under the subheading, ‘Symptom importance’ ,\n“Figure 1 shows a heatmap of the Jaccard Index values, indicating the correlation between each pair of symptom \nvalues. In this figure, the yellow rows/columns indicate that the symptom is highly correlated with many other \nsymptoms. We identified six symptoms that are highly correlated (Jaccard Index > 0.8) with more than 30% of \nthe symptoms: fever, abnormal uterine bleeding, syncope (fainting, passing out), infertility, constant bleeding, \nand malaise/sickness. Five of these symptoms appear at the bottom of Table 1. To investigate whether removing \nthese potentially redundant features improves the models’ classification performance, we trained the models again \nwithout these six symptoms. Table 3 present the performance results of the different models. After removing the \nhighly correlated symptoms, the performance of the Decision Tree model improved, whereas the performance \nof the remaining models diminished slightly. ”\nnow reads:\n“Figure 1 shows a heatmap of the Jaccard Index distance values, derived as 1-Jaccard index, which reflect the \ncorrelation levels between symptom pairs. In this figure, darker cells signify smaller distances, indicating a higher \ndegree of similarity between the symptoms. Notably, all calculated Jaccard distance values exceeded 0.25, and \nfollowing this analysis, no columns were eliminated due to redundancy. ”\nAdditionally, in section “Symptom importance analysis” , the sentence “We analyzed the performance of the \nmodels after removing symptoms that are highly correlated with other symptoms (Jaccard Index close to 1). ” \nwas removed.\nThirdly, in the legend of Figure 1:\n“ A heatmap that shows Jaccard Indices between each pair of symptom value vectors. A lighter color indicates \na higher Jaccard Index, or a strong similarity between values. We use the Jaccard Index to identify potentially \nredundant symptoms. ”\nnow reads:\n“ A heatmap that shows Jaccard Indices between each pair of symptom value vectors. A darker color indicates \na lower Jaccard Index distance, or a strong similarity between symptoms. We use the Jaccard Index to identify \npotentially redundant symptoms. ”\nLastly, Table 3 was removed.\nThe original Article has been corrected.\nOPEN\n\n\n2\nVol:.(1234567890)Scientific Reports |        (2024) 14:10443  | https://doi.org/10.1038/s41598-024-61280-3\nwww.nature.com/scientificreports/\nOpen Access  This article is licensed under a Creative Commons Attribution 4.0 International \nLicense, which permits use, sharing, adaptation, distribution and reproduction in any medium or \nformat, as long as you give appropriate credit to the original author(s) and the source, provide a link to the \nCreative Commons licence, and indicate if changes were made. The images or other third party material in this \narticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the \nmaterial. If material is not included in the article’s Creative Commons licence and your intended use is not \npermitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from \nthe copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.\n© The Author(s) 2024","source_license":"CC-BY-4.0","license_restricted":false}