A decision-making mechanism for assessing risk factor significance in cardiovascular diseases

Cardiovascular diseases (CVDs) are severe diseases whose growing incidence worldwide has spurred increased national healthcare spending. Despite numerous diagnostic and treatment suggestions, CVDs continue to merit investigation due to their diverse risk factors, some of which are positively, negatively, or not correlated. To assist doctors and researchers in identifying the significance of CVD risk factors, in this study we propose a novel ranking and attribute (or feature) selection algorithm. We applied seven popular machine learning technologies to generate attribute-ranked datasets in order to identify the ideal number of factors/attributes for each classifier. Above all, the results of the comparisons indicate that the performance of parts of factors after ranking and attribute selection was significantly better than the performance of whole factors and that of several state-of-the-art algorithms. Since such knowledge can aid the proper selection of factors of CVD patients and thereby assist doctors in making better decisions in diagnostics and treatment, our results can reduce treatment costs and thus lower the economic burden of healthcare.