A novel approach to venous clinical severity score prediction: combining metaheuristic algorithm and random forest classification
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Updated: Sep 19, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Hao Zhu1, Nianyun Zhang2, Yuanzhen Ni3
1Physical Education of Nanjing Xiaozhuang University, Nanjing, China.
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.
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Venous Return
What is Venous Return?
Venous return refers to the rate at which blood flows back to the heart from the body's peripheral veins. It's an integral part of the circulatory system...