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However, the problem of sample imbalance often exists in the medical field, which leads to a decrease in  classification performance of the machine learning.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>To solve the problem of sample imbalance in medical dataset, we propose a hybrid sampling algorithm combining synthetic minority over-sampling technique (SMOTE) and edited nearest neighbor (ENN). Firstly, the SMOTE is used to over-sampling missed abortion and diabetes datasets, so that the number of samples of the two classes is balanced. Then, ENN is used to under-sampling the over-sampled dataset to delete the \"noisy sample\" in the majority. Finally, Random forest is used to model and predict the sampled missed abortion and diabetes datasets to achieve an\u00a0accurate clinical diagnosis.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Experimental results show that Random forest has the best classification performance on missed abortion and diabetes datasets after SMOTE-ENN sampled, and the MCC index is 95.6% and 90.0%, respectively. In addition, the results of pairwise comparison and multiple comparisons show that the SMOTE-ENN is significantly better than other sampling algorithms.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Random forest has significantly improved all indexes on the missed abortion dataset after SMOTE-ENN sampled.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-02075-2","type":"journal-article","created":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T13:08:16Z","timestamp":1672319296000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["A hybrid sampling algorithm combining synthetic minority over-sampling technique and edited nearest neighbor for missed abortion diagnosis"],"prefix":"10.1186","volume":"22","author":[{"given":"Fangyuan","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisha","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengjiao","family":"Zhai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiejie","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"issue":"11","key":"2075_CR1","doi-asserted-by":"publisher","first-page":"5540","DOI":"10.1109\/JBHI.2022.3182722","volume":"26","author":"B Pu","year":"2022","unstructured":"Pu B, Lu Y, Chen J, et al. 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The institutional review board of the First Affiliated Hospital of Henan Polytechnic University approved this study (No. 2022-03-01). Informed consent was obtained from patients or their family members.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"As the manuscript does not contain data from any individual person, So \u201cNot applicable\u201d in this section.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"344"}}