A novel approach to venous clinical severity score prediction: combining metaheuristic algorithm and random forest classification

Varicose veins stem from valve failure, with conventional treatments offering limited relief. Yoga, along with lifestyle and dietary changes, may help prevent and improve the condition. This study used Random Forest Classification to predict VCSS, a standard measure of chronic venous insufficiency s...

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Vydáno v:	Computer methods in biomechanics and biomedical engineering s. 1 - 15
Hlavní autoři:	Zhu, Hao, Zhang, Nianyun, Ni, Yuanzhen, Sun, Qiang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England 16.06.2025
Témata:	improved arithmetic optimization algorithm random forest classification Venous clinical severity score beluga whale optimization
ISSN:	1025-5842, 1476-8259, 1476-8259
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Varicose veins stem from valve failure, with conventional treatments offering limited relief. Yoga, along with lifestyle and dietary changes, may help prevent and improve the condition. This study used Random Forest Classification to predict VCSS, a standard measure of chronic venous insufficiency severity. BWO and IAOA optimizers enhanced model performance, evaluated across four VCSS categories: absent, mild, moderate, and severe. The RFBW hybrid model, combining RFC and BW, showed the highest accuracy, supported by high precision scores of 0.917, 0.952, 0.976, and 1.000, highlighting its efficiency and reliability. Notably, the RFIA model showed results similar to the RFBW model.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1025-5842 1476-8259 1476-8259
DOI:	10.1080/10255842.2025.2514133