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. 2022 Feb 10;12:2301. doi: 10.1038/s41598-022-06274-9

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Testing the multiclass model for its ability to distinguish the individual cancer groups. Panel (A) shows the results of specifically testing the multiclass trained model for separation of endometrial cancer samples from the other cancers (breast, cervical, ovarian) based on model’s Endometrial scores. The resulting confusion matrix on applying a threshold shows good accuracy, sensitivity and specificity. Panel (B) shows the results of specifically testing the multiclass trained model for separation of breast cancer samples from the other cancers (endometrial, cervical, and ovarian) based on model’s Breast scores. The resulting confusion matrix on applying a threshold shows good accuracy, sensitivity and specificity. Panel (C) the results of specifically testing the multiclass trained model for separation of cervical cancer samples from the other cancers (breast, endometrial, ovarian) based on model’s Cervical scores. The resulting confusion matrix on applying a threshold shows good accuracy, sensitivity and specificity. Panel (D) shows the results of specifically testing the multiclass trained model for separation of ovarian cancer samples from the other cancers (breast, endometrial, cervical) based on model’s Ovarian scores. The resulting confusion matrix on applying a threshold shows high accuracy, sensitivity and specificity.