Hybrid decision making in the monitoring of hypertensive patients

In the intelligent monitoring of the hypertensive patients, it´s necessary to assess their treatment effect and give corresponding diagnostic feedback automatically. This paper proposed a hybrid decision support system (DSS) combining several data mining techniques using an improved weighted majority voting scheme (iWMV). The mass health data of hypertensive patients were used as data source of the data mining techniques, and iWMV was used to produce a proper final judgement on patients´ control condition on the basis of the individual classifier results. The proposed system was trained and evaluated using data from 167 hypertensive patients. Performance analysis showed that the hybrid system could reach classification rate (CR) of 95.34% and kappa coefficient (KC) of 92.54%, much better than systems with a single classification algorithm or combining using the simple weighted majority voting scheme (WMV). Moreover, the proposed DSS showed high stability.