Machine Learning Approach to find the relation between Endometriosis, benign breast disease, cystitis and non-toxic goiter

Jung Hun Lee, Seon-Young Kwon, Ji-Ho Chang, Jiho Chang, Jin‐Sung Yuk
Scientific reports · 2019 · vol. 9(1) , pp. 5410 · doi:10.1038/s41598-019-41973-w · PMID:30932000 · PMC6443655 · W2931225169
article OA: gold CC0 ⤵ 11 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This study applied a machine learning recommendation system to South Korean insurance data and found associations between endometriosis and benign breast disease, cystitis, and non-toxic goiter.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-07 ⓘ

This study used a recommendation-system approach based on item similarities to identify diseases associated with endometriosis in South Korea using HIRA-NIS insurance data from women ages 15–45 (2009–2015). Endometriosis was defined by ICD-10 code N80.x plus concurrent treatment, while the control group included women without N80.x; benign breast disease, cystitis, and non-toxic single thyroid nodule were among the diseases prioritized by the machine-learning similarity model and then tested with adjusted weighted logistic regression. Endometriosis was statistically associated with benign neoplasms of the breast (OR 2.58), other cystitis (OR 2.63), and non-toxic single thyroid nodule (OR 1.62). A key limitation is that the recommendation model considered only 30 arbitrarily limited candidate disease categories and the RS model showed low mean precision and recall (0.032 and 0.036). This paper is centrally about endometriosis — it applies a machine-learning recommender system to insurance claims data to identify comorbid associations with endometriosis, including benign breast disease, cystitis, and non-toxic goitre.

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Abstract

The exact mechanism of endometriosis is unknown. The recommendation system (RS) based on item similarities of machine learning has never been applied to the relationship between diseases. The study aim was to identify diseases associated with endometriosis by applying RS based on item similarities to insurance data in South Korea. Women aged 15 to 45 years extracted from the Korean Health Insurance Review & Assessment Service National Inpatient Sample (HIRA-NIS) 2009-2015. We used the RS model to extract diseases that were correlated with an endometriosis diagnosis. Among women aged 15 to 45 years, endometriosis was defined as a diagnostic code of N80.x and a concurrent treatment code. A control group was defined as women who did not have the N80.x code. Benign breast diseases, cystitis, and non-toxic goitre were extracted by the RS. A total of 1,730,562 women were selected as the control group, and 11,273 women were selected as the endometriosis group. In logistic regression analysis adjusted for age per 5 years, data year, and socioeconomic status, benign neoplasm of breast (odds ratio (OR): 2.58; 95% confidence interval (CI): 1.90-3.50), other cystitis (OR: 2.63; 95% CI: 1.56-4.44), and non-toxic single thyroid nodule (OR: 1.62; 95% CI: 1.14-2.32) were statistically significant. Endometriosis was associated with benign breast disease, cystitis, and non-toxic goitre.

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Condition tags

endometriosis

MeSH descriptors

Cystitis Endometriosis Fibrocystic Breast Disease Goiter Machine Learning Adolescent Adult Cross-Sectional Studies Cystitis Diagnosis, Differential Endometriosis Female Fibrocystic Breast Disease Goiter Humans Logistic Models Middle Aged Reproducibility of Results Sensitivity and Specificity Young Adult

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