Abstract
Often datasets may involve thousands of attributes, and it is important to discover relevant features for machine-learning (ML) algorithms. Here, approaches that reduce or select features may become difficult to apply, and feature discovery may be made using frequent-set mining approaches. In this paper, we use the Apriori frequent-set mining approach to discover the most frequently occurring features from among thousands of features in datasets where patients consume pain medications. We use these frequently occurring features along with other demographic and clinical features in specific ML algorithms and compare algorithms’ accuracies for classifying the type and frequency of consumption of pain medications. Results revealed that Apriori implementation for features discovery improved the performance of a large majority of ML algorithms and decision tree performed better among many ML algorithms. The main implication of our analyses is in helping the machine-learning community solves problems involving thousands of attributes.
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Notes
- 1.
Due to a non-disclosure agreement, we have anonymized the actual names of these medications.
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Acknowledgement
The project was supported by grants (awards: #IITM/CONS/PPLP/VD/03 and # IITM/CONS/RxDSI/VD/16) to Varun Dutt.
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Kaushik, S., Choudhury, A., Dasgupta, N., Natarajan, S., Pickett, L.A., Dutt, V. (2018). Evaluating Frequent-Set Mining Approaches in Machine-Learning Problems with Several Attributes: A Case Study in Healthcare. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_20
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