Early Detection of Cardiovascular Disease in Patients with Chronic Kidney Disease using Data Mining Techniques

Abstract

 A constant obstacle for doctors is the high prevalence of cardiovascular disease (CVD) in patients with chronic kidney disease (CKD). Increasing efforts have been made to jointly treat patients with heart and kidney disease, as shown by an increasing number of basic research and clinical investigations concerning CVD in CKD. Typical risk factors for CVD are common in CKD, such as age, blood pressure (bp), hypertension (htn), and blood sugar (sg). Standard risk factors tend to be the major contributors to CVD in patients with mild to moderate CKD. However, in patients with advanced CKD, non-traditional CKD-specific risk factors (e.g. Potassium level in blood) are more prevalent than in the general population, contributing, in addition to traditional risk factors, to the high burden of CVD in CKD. However, in patients with CKD, CVD often remains underdiagnosed and undertreated. Nevertheless, CVD still remains under control and care in patients with CKD. Researchers in this paper aims to predict the probability of CVD from CKD by using various popular data mining techniques and definitively propose a decision tree and by using Random Forest analysis to test its specificity and sensitivity to achieve concrete results with sufficient precision.