Detecting factors associated with polypharmacy in general practitioners' prescriptions: A data mining approach

Prescribing and consuming drugs more than necessary is considered polypharmacy, which is both wasteful and harmful. The purpose of this paper is to establish an innovative data mining framework for analyzing physicians' prescriptions regarding polypharmacy. The approach consists of three main steps: pre-modeling, modeling, and post-modeling. In the rst step, after collecting and cleaning the raw data, several novel features of physicians are extracted. In the modeling step, two popular decision trees, i.e., C4.5 and Classification And Regression Tree (CART), are applied to generate a set of If-Then rules in a tree-shaped structure to detect and describe physicians' features associated with polypharmacy. In a novel approach, the Response Surface Method (RSM) as a tool for hyper-parameter tuning is simultaneously applied along with Correlation-based Feature Selection (CFS) to enhance the performance of the algorithms. In the post-modeling step, the discovered knowledge is visualized to make the results more perceptible and is, then, presented to domain experts to evaluate whether they make sense or not. The framework was applied to a real-world dataset of prescriptions. The results were con rmed by the experts, which demonstrated the capabilities of the data mining framework in the detection and analysis of polypharmacy. The derived If-Then rules can be bene cial for healthcare managers and policy-makers to recognize physicians' prescribing patterns and take suitable actions to support medicine management and develop high-quality prescribing guidelines. © 2022 Sharif University of Technology. All rights reserved.