AGGREGATION OF HETEROGENEOUS DATA IN ATHEROSCLEROSIS ASSESSMENT

Abstract: ‘The cardiovascular disease atherosclerosis can lead to life threatening conditions such as stroke. Th‘is study explores supervised learning to assess atherosclerosis in individuals, using heterogeneous data in the form of tabular data from several data sets and images. Aggregation of the data by utilizing data fusion, pre-processing techniques, di‚fferent learning methods and di‚fferent decision fusion approaches were explored in order to propose an architecture with focus on high accuracy on assessing atherosclerosis. Using a support vector machine for a concatenation of pre-processed tabular data, and a convolutional neural network for a pre-processed image, weighted majority voting on these models’ intermediatepredictions to produce a €final prediction yielded an accuracy of 89:20%. A generalized version of this architecture, which can address the task of classifi€cation with similar heterogeneous data, is also introduced. Both the architecture and the generalized version of the architecture di‚ffer from traditional methods o faddressing similar assessment tasks, which only considers homogeneous data.