Data mining perspective: Prognosis of life style on hypertension and diabetes

In the present era, the data mining techniques are widely and deeply useful as decision support systems in the fields of health care systems. The proposed research is an interdisciplinary work of informatics and health care, with the help of data mining techniques to predict the relationship among interventions of hypertension and diabetes. As the study shows persons who have diabetes can have chances of hypertension and vice versa. In the present work we would like to approach the life style intervention of hypertension and diabetes and their effects using data mining. Life style intervention plays a vital role to control these diseases. The intervention includes the risk factor like diet, weight, smoking cessation and exercise. The regression technique is used in which dependent and Independent Variables (IV) are defined. The four interventions are treated as (IV) and two diseases hypertension and diabetes are Dependent Variables (DV). We have established the relationship between hypertension and diabetes, using the data set of Non Communicable Disease (NCD) report of Saudi Arabia, World Health Organisation’s (WHO). The Oracle Data Miner (ODM) tool is used to analyse the data set. Predictive data analysis gives the result that interventions weight control and exercise have the direct relationship between them in both the diseases.