A proposed Mining System for Recognition of the Peripheral Arterial Disease and Analyzing Risk Factors

Abstract

Association rule techniques is considered as one of the most well-known techniques which has been employed to find the most dominant associations and correlations of the items within large data set. Thus, it can be used as useful method to analyze and fix many data mining issues. In this research, the association rule has been adopted as a proposal method for the cases that are related to peripheral arterial disease (PAD) risk factor.
 PAD is one of the most common problem with high morbidity and mortality, which has many potential risks factors that might affect its severity. Such factors can be modified, and beneficial to good assessment and diseases’ progress control and the effect of those treatments and its modalities, by the understanding of some relations between those factors. The gender factor among other factors was not discussed because the ratio of male to female is almost the same. Our data collected out from 250 patient persons with PAD which was analyzed and recognized. By the use of the proposed method that implemented within two stages: firstly we implemented stage a back propagation-NN was trained on records of patients with PAD, and secondly the association rules encoding was performed, here the relations between pre-disposing factors was extracted.
 Our proposed miming system offers a faster and better data analysis and recognition; also it reduces time, efforts, and also explores the relations between factors. Therefore, we got the relations those been mentioned above, in which they are important in the process of PAD management in the future.
 There was a strong relations among alcoholism, smoking ,diabetes and