Efficient Algorithms for Patterns Identification in Medical Data

Avram Calin; Adrian Gligor; Victoria Nylas; Roman Dumitru

doi:10.2478/amset-2023-0006

Abstract

Recently, medical databases have expanded rapidly, and the amount of information is huge. This abundance of data appears as a consequence of the new technologies that have been developed in the medical field and that allow easy data collection. The performance of the technique depends on the input data and available resources. Whereas, in Eclat the repeated scanning of the database is eliminated and consumes less time and we can conclude that Eclat is better than Apriori and Fpgrowth. If we refer to the execution time and memory usage, then the FP-Growth algorithm is more efficient than the Eclat algorithm or the Apriori algorithm. If we consider factor other than time, the result may vary from one factor to another.

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Efficient Algorithms for Patterns Identification in Medical Data

Abstract

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