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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Vydané v:Computer methods in biomechanics and biomedical engineering s. 1 - 15
Hlavní autori: Zhu, Hao, Zhang, Nianyun, Ni, Yuanzhen, Sun, Qiang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England 16.06.2025
Predmet:
improved arithmetic optimization algorithm
random forest classification
Venous clinical severity score
beluga whale optimization
ISSN:1025-5842, 1476-8259, 1476-8259
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Abstract 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.
AbstractList 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.
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.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.
Author Zhang, Nianyun
Sun, Qiang
Ni, Yuanzhen
Zhu, Hao
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Keywords improved arithmetic optimization algorithm
random forest classification
Venous clinical severity score
beluga whale optimization
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Title A novel approach to venous clinical severity score prediction: combining metaheuristic algorithm and random forest classification
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