A Study to Classify Non-Dipper/Dipper Blood Pressure Pattern of Type 2 Diabetes Mellitus Patients without Holter Device

Abstract

The aim of this study was to design an expert system to predict the Non-Dipping or Dipping pattern by using several basic clinical and laboratory data through an artificial intelligence algorithm. Data Mining is a technique which extracts information from data sets by using a combination of both statistical analysis methods and artificial intelligence algorithms. Also in this study, the decision tree and naivebayes classification algorithms of this technique were used. Firstly, sixty-five patients (mean age 51 +/- 7 years, 40 females,) were included in the study. Systolic and diastolic dipping were found in 13 and 15 % of the patients, respectively. In the advancing process of the experiment, the number of instances were reduced, because of some missing data of the patients. The data sets were tested using the J48 decision tree algorithm. This classification algorithm was implemented on 56 instances, and also the number of attributes was reduced from 35 to 23. 66 % of the instances (37) were reserved for training and 44 % of the instances (19) were reserved for testing. When the algorithm was run, the Non-Dipper/Dipper pattern of the instances were correctly predicted in a rate of 73.6842 %. Model was built in 0.02 seconds. This pilot study shows that a machine learning algorithm can help in the prediction of diurnal blood pressure pattern relying on some basic demographic, clinical and laboratory data, with a reasonable accuracy.