Prediction of Risk Takers in Arterial Hypertension Patients with Data Mining Application

Abstract

Hyper arterial pressure (HAP) is a disease that kills silently because it does not produce symptoms in the early stages, making it difficult to diagnose. When it is detected, its treatment is not accessible to everyone, which affects the disease's long-term development. Hypertension affects a large portion of the Iraqi population. In the current research paper, we have discussed how data mining can be applied to identify the status of the risk factors that affect arterial hypertension due to I10-I15 causes, evaluating the context variables disability, overwork, high-risk pregnancy, stress, high diets, and poor nutrition in the population between 50 and 64 years in the city of Baghdad. It is possible to see how data mining in large volumes of health data can generate new knowledge and thus uncover hidden patterns in the data through the development of this research. Attributes directly linked to disease prevalence can be found in data from Baghdad, Iraq, even if they are not directly linked to a specific cause. This shows that some variables are transversal to the development of the disease regardless of its categorization. Cluster analysis revealed that, even though these diseases are categorized as having different causes, they have a degree of incorrect classification of 40.71% because they present attributes with a similar behavior transversal to the disease and not the disease-specific cause for which it is categorized.