A computational algorithm for the risk assessment of developing acute coronary syndromes, using online analytical process methodology

This paper investigates patterns in cardiovascular risk factors from a large population sample of cardiac patients and their matched controls. Various factors were taken into consideration and were used as inputs to effectively demonstrate online analytical process, OLAP methodology. OLAP is a new method that is used to explore the role of several risk factors in cardiovascular disease risk assessment. It equally serves as a means to extract knowledge from the investigated factors' levels. This paper discusses the application of OLAP-specific procedures in order to explore hidden pathways associated with risk factors among patients and controls. It does so, as the latter proves to be time consuming when classical statistical methods, in particular logistic regression are applied. Finally, this work builds on earlier findings, with odds ratios converging among the studies. The outcome of this work results in a more accurate risk assessment, as it takes into account variable-interaction.