{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T02:39:55Z","timestamp":1792636795742,"version":"4.5.0"},"reference-count":20,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Phlebology"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>This study aimed to develop a web-based machine learning (ML) model to predict the lifetime likelihood of developing varicose veins using global disease prevalence data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We utilized data from a systematic review, registered under PROSPERO (CRD42021279513), which included 81 studies on varicose vein prevalence across various geographic regions. The data used to build the ML model included disease prevalence as the outcome (%), along with the following predictors: mean age, gender distribution (%), mean body mass index (BMI) of the study cohort, and the mean gravity field of the study region (mGal), representing variations in Earth\u2019s underground mass distribution that influence blood and fluid redistribution in the human body, affecting disease prevalence. After standardizing the outcome and predictors, the model was trained using neural network regression implemented with the TensorFlow.js library and deployed as a web-based ML application.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>After 406 epochs of training, and upon achieving a validation loss (mean squared error) of 0.9, training was stopped due to no further improvement. The achieved test loss was 0.49, and the mean absolute error (MAE) was 0.56, corresponding to an up to 6.7% difference between the predicted and true disease probabilities (calculated as MAE x \u03c3, where \u03c3 is the standard deviation of the mean disease prevalence = 0.56 x 11.9 = 6.7). The likelihood of developing varicose veins, as predicted by the model, showed the strongest correlation with age (0.78), followed by gravity anomaly (0.30), BMI (0.27), and gender (0.15).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study summarizes research on the prevalence of varicose veins by developing a web-based ML model to predict an individual\u2019s likelihood of developing the disease. Using data reported in the literature, the ML algorithm provides a non-discriminatory predictive baseline, offering a valuable tool for future investigations into disease epidemiology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/02683555251318154","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T18:49:21Z","timestamp":1738176561000},"page":"528-535","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Machine learning web application for predicting varicose veins utilizing global prevalence data"],"prefix":"10.1177","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9128-2191","authenticated-orcid":false,"given":"Yury","family":"Rusinovich","sequence":"first","affiliation":[{"name":"University Hospital Leipzig"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0757-1987","authenticated-orcid":false,"given":"Volha","family":"Rusinovich","sequence":"additional","affiliation":[{"name":"University Hospital Leipzig"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Doss","sequence":"additional","affiliation":[{"name":"University Hospital Leipzig"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,1,29]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejvs.2021.12.024"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1097\/SLA.0000000000004631"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0037-198X(98)80001-1"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1177\/026835550001500201"},{"key":"e_1_3_4_6_2","unstructured":"Tensorflow. 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TensorFlow.js is a library for machine learning in JavaScript. https:\/\/www.tensorflow.org\/js (Accessed July 24 2024)."},{"key":"e_1_3_4_12_2","unstructured":"GitHub. Machine learning application for predicting varicose veins. https:\/\/github.com\/vokiru\/varicose (Accessed January 2 2025)."},{"key":"e_1_3_4_13_2","unstructured":"Rusinovich Y. Varicose veins prediction. https:\/\/mlhs.ink\/varicose\/ (Accessed January 3 2025)."},{"key":"e_1_3_4_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvsv.2019.12.075"},{"key":"e_1_3_4_15_2","doi-asserted-by":"publisher","DOI":"10.1024\/0301-1526\/a000222"},{"key":"e_1_3_4_16_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.849027"},{"key":"e_1_3_4_17_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2018.0246"},{"key":"e_1_3_4_18_2","unstructured":"Kaggle. Datase: health. https:\/\/www.kaggle.com\/datasets?search=health (Accessed January 8 2025)."},{"key":"e_1_3_4_19_2","unstructured":"Hugging Face. 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