CVD level prediction processor using DNA computing

Abstract

We propose a CVD (Cardiovascular disease) level prediction processor which has reduced computational efforts and time with insignificant diminution of CVD diagnosis accuracy when compared to the conventional CVD level prediction methods. The proposed DNA computing algorithm for implementation of CVD level prediction processor uses masks for simple nonspecific hybridization. Using the proposed mask, we can reduce HW computational efforts. Besides this, we decrease processing time resulting by co-processing hybridizations with a number of parallel matching units. The proposed CVD level prediction processor diagnoses CVD data of patients within 3.4 us. Therefore, the proposed CVD level prediction processor can be applied in a CVD level diagnosis system.