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Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for Predicting Obstructive Sleep Apnea

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An improved Boosting algorithm, named as Boosted PARM-DT, was developed by pre-pruning techniques and Associative Rule Mining (ARM) on decision trees built from the clinical datasets a collected for Obstructive Sleep Apnea (OSA). The Pruned-Associative-Rule-Mined Decision Trees (PARM-DT) developed by adopting pre-pruning techniques on tree depth, minimum leaf and/or parent node size observations and maximum number of tree splits, based on Apriori and/or Adaptive Apriori (AA) frameworks, is boosted to achieve better predictive accuracies. The improved algorithms were implemented in OSA dataset and UCI online databases for comparisons. Better predictive accuracies were achieved in all the applied datasets/databases when comparing the classical algorithm, i.e., Boosted DT, with the improved one, i.e., Boosted PARM-DT.
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Keywords: Adaptive Apriori (AA); Apriori; Associative Rule Mining; Boosted PARM-DT; Pre-Pruning Techniques

Document Type: Research Article

Affiliations: 1: Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, Kuching, Sarawak, Malaysia 2: Respiratory Medicine Unit, Department of Respiratory Medicine, UiTM Medical Specialist Centre, Faculty of Medicine, Universiti Teknologi MARA, Selangor, Malaysia

Publication date: November 1, 2017

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