Multi-dimensional proprio-proximus machine learning for assessment of myocardial infarction

Abstract This work presents a novel analysis methodology that utilises high-resolution, multi-dimensional information to better classify regions of the left ventricle after myocardial infarction. Specifically, the focus is to determine degree of infarction in regions of the left ventricle based on information extracted from cardiac magnetic resonance imaging. Enhanced classification accuracy is achieved using three mechanisms: Firstly, a plurality of indices/features is used in the pattern classification process, rather than a single index/feature (hence the term “multi-dimensional). Secondly, the method incorporates not only the indices/features of the region in consideration, but also indices/features from the neighbouring regions (hence the term “ proprio-proximus ”). Thirdly, advanced machine learning techniques are used for both feature selection and pattern classification process to ameliorate the effect of class-imbalance existing in the data. Numerical results from multiple experiments on real data showed that using multiple features improved the ability to distinguish between infarcted and non-infarcted remote segments, and using neighbouring information improved classification performance. The proposed methodology is general and can be adapted for the analysis of biological functions of other human organs.