Comparing Association Rules and Deep Neural Networks for Heart Disease Prediction

Two decades ago, the most popular data mining technique were association rules (ARs). Nowadays deep neural networks (DNNs) are the most popular mechanism for building predictive models. On the other hand, medical data sets, despite being generally small in size (low volume), they are challenging for predictive models due to diverse attribute content (high variety) and variables with low redundancy (high variability). In this work we compare these two analytic techniques to identify effective models to predict heart disease, a multi-target prediction problem. Both techniques require expertise, manual tuning, and iterative experimentation to determine optimal parameters. Our goal is to build a DNN model that is at least as good as the best ARs. There exist two Big Data challenges: risks factors combined with imaging attributes produce a large number of hidden patterns and the number of association rules reaches millions, without using search constraints at low support (frequency) values. Preliminary experiments on a real data set show discovered rules have high predictive accuracy and they provide a highly accurate, but highly specific, profile of sick patients. Despite careful data pre-processing and hyper-parameter tuning DNNs are slightly more accurate than association rules, but more generalizable. Therefore, both techniques can complement each other.