Application of Clustering as a Data Mining Tool in Bp systolic diastolic


This work demonstrates the application of clustering , a data mining tool, in the health care system. Hemoglobin A1c is the most parameters for the monitoring of metabolic control of patients with diabetes mellitus[1]. The aim of this study is to determine the reference rang of glycosylated hemoglobin (Hb A1c%) in an Iraqi population (males and females ) effect and predict Bp systolic diastolic( Blood pressure systolic diastolic) by using demonstrates the application of clustering, as data mining tool, in the health care system. Data mining has the capability for clustering, prediction, estimation, and pattern recognition by using health databases.Blood samples were collected from 100 healthy subjects ( 50 females and 50 males ) are ranged between (20-75) years old as dataset. The reference value of HbA1c% was (5.34 + 0.67)% in female and (5.67 + 0.73)% in males. The present clustering and found a strong relation between HbA1c% and systolic diastolic blood pressure in males whereas the relation in females no significant