Predicting heart failure class using a sequence prediction algorithm

Rjeily, Carine Bou; Badr, Georges; Hassani, Amir Hajjam Al; Andrès, Emmanuel

doi:10.1109/icabme.2017.8167546

Cited by 9 publications

(1 citation statement)

References 7 publications

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“…Various sequence prediction models have been proposed, having various characteristics. They have been used in many domains to perform tasks such as predicting heart failure [18], human activities [19] and webpage prefetching [6]. Although numerous prediction models have been proposed, many are lossy models [3,9,15,16,21].…”

Section: Introductionmentioning

confidence: 99%

Succinct BWT-Based Sequence Prediction

Ktistakis

Fournier-Viger

Puglisi

et al. 2019

Lecture Notes in Computer Science

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Sequences of symbols can be used to represent data in many domains such as text documents, activity logs, customer transactions and website click-streams. Sequence prediction is a popular task, which consists of predicting the next symbol of a sequence, given a set of training sequences. Although numerous prediction models have been proposed, many have a low accuracy because they are lossy models (they discard information from training sequences to build the model), while lossless models are often more accurate but typically consume a large amount of memory. This paper addresses these issues by proposing a novel sequence prediction model named SuBSeq that is lossless and utilizes the succinct Wavelet Tree data structure and the Burrows-Wheeler Transform to compactly store and efficiently access training sequences for prediction. An experimental evaluation shows that SuBSeq has a very low memory consumption and excellent accuracy when compared to eight state-of-the-art predictors on seven real datasets.

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