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https://dspace.chmnu.edu.ua/jspui/handle/123456789/1881
Titel: | Data Mining of Ambulatory Blood Pressure Monitoring |
Autoren: | Chuiko, G. Darnapuk, Y. Dvornik, O. Gravenor, M. Yaremchuk, O. |
Stichwörter: | ambulatory blood pressure monitoring data mining data set clustering machine learning algorithms |
Erscheinungsdatum: | 2023 |
Herausgeber: | Institute of Electrical and Electronics Engineers Inc. |
Zusammenfassung: | The paper's aim is data mining of the recently published (2017) Ambulatory Blood Pressure Monitoring database, which covers 270 patients. Preprocessing, denoising, unsupervised Machine Learning (clustering), and feature selection were performed within Java-based software (WEKA 3-9-6). The Blood Pressure Load Level showed the best congruence with the obtained clusters. It also has the lowest noise among other levels of the database. The methods are designed to simplify database mining for clinicians largely unfamiliar with machine learning algorithms and benefit from simple algorithm approaches and visual aids output. |
Beschreibung: | Chuiko, G., Darnapuk, Y., Dvornik, O., Gravenor, M., & Yaremchuk, O. (2023). Data Mining of Ambulatory Blood Pressure Monitoring. 2023 13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023. Athens. DOI: 10.1109/DESSERT61349.2023.10416531 |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185836998&doi=10.1109%2fDESSERT61349.2023.10416531&partne https://ieeexplore.ieee.org/document/10416531 https://dspace.chmnu.edu.ua/jspui/handle/123456789/1881 |
ISBN: | 979-8-3503-9612-6 e-ISBN 979-8-3503-9611-9 |
Enthalten in den Sammlungen: | Публікації науково-педагогічних працівників ЧНУ імені Петра Могили у БД Scopus |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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Chuiko, G., Darnapuk, Y., Dvornik, O., Gravenor, M., Yaremchuk, O..pdf | 59.4 kB | Adobe PDF | Öffnen/Anzeigen |
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