In this paper, parallelism methodologies for the mapping of machine learning algorithms derived rules on both software and hardware are investigated. Feeding the input of these algorithms with patient diseases data, medical diagnostic decision trees and their corresponding rules are outputted. These rules can be mapped on multithreaded object oriented programs and hardware chips. The programs can simulate the working of the chips and can exhibit the inherent parallelism of the chips design. The circuit of a chip can consist of many blocks, which are operating concurrently for various parts of the whole circuit. Threads and inter‐thread communication can be used to simulate the blocks of the chips and the combination of block output signals. The chips and the corresponding parallel programs constitute medical classifiers, which can classify new patient instances. Measures taken from the patients can be fed both into chips and parallel programs and can be recognized according to the classification rules incorporated in the chips and the programs design. The chips and the programs constitute medical decision support systems and can be incorporated into portable micro devices, assisting physicians in their everyday diagnostic practice.

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