Real-Time Epidemiology Of Varicose Veins And Chronic Venous Disease Prediction Using Decision Tree Algorithm

Abstract

This paper presents a system for identifying the veins that is suffered from the chronic venous disease called the varicose disease. Mostly, the physician determines whether there are varicose veins in human leg through diagnosis of images collected through transducer. Hence the automatic diagnosis has gained extensive attention. The varicose veins are the lesion veins that are swollen and twisted veins that usually occur in lower limbs. This disease occurs due to damaged or weak valves of the veins that lead to improper flow of blood against gravity. In this paper, a wearable sock with sensors based on non-invasive diagnostic and therapeutic solution is provided to predict and prevent the varicose veins at early stage. This project proposes a varicose vein detection algorithm based on decision tree in machine learning concept. Based on the values from the sensors, the dataset is predefined. The acquired positional is processed using Arduino-uno and decision tree algorithm from machine learning concept.