Feature Selection Using Automatic Programming Methods in Hypertension Risk Prediction

Hypertension is a condition where the pressure in the blood vessels is higher than normal. It can lead to serious problems such as heart attack, stroke, heart failure, kidney disease and vision problems. Therefore, early diagnosis and treatment is important to find appropriate treatment strategies for the disease. In this study, automatic programming (AP) methods, were used and compared to analyze the risk of hypertension. These methods are Artificial Bee Colony Programming developed from the behavior of honeybees, Genetic Programming (GP) inspired by genetic selection and Immune Plasma Programming (IPP) based on immune plasma therapy. According to the performance evaluations obtained from the methods, GP and IPP were the most successful methods with test success rates of 0.91% and 0.89% respectively. In future research, Due to the success of the AP methods, we aim to develop different versions for health problems.