An ensemble based on distances for a kNN method for heart disease diagnosis
作者： Alberto Palacios Pawlovsky
DOI:
关键词: Artificial intelligence 、 Heart disease 、 Medical diagnosis 、 Approximation theory 、 k-nearest neighbors algorithm 、 Decision tree 、 Pattern recognition 、 Computer science 、 Data set
摘要: This paper introduces an ensemble based on distances for a kNN (k Nearest Neighbor) method and shows results of its application to heart disease diagnosis. The has been implemented with two configurations. One using three another one five. We also added them weighted version the average accuracy that each distance gives when used in method. Our gave nearly 85% any configurations versions we tested UCI Cleveland data set.
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