A metaheuristic-based stacking model for predicting the risk of patient no-show and late cancellation for neurology appointments

Abstract

Patient no-shows and late cancellations for an appointment are common problems in healthcare, which adversely affect the financial performance and quality of service of healthcare organizations. A high rate of patient no-show and late cancellation in a clinic can significantly limit access to healthcare. In general, hospitals create predictive models to assess risk of no-show, and then assign overbooking appointments utilizing those risks. In this paper, by incorporating machine learning and optimization techniques, we proposed a predictive model to assist with the overbooking decision. The model consists of two phases. First, we utilized a metaheuristic optimization technique to explore the best subset of featuresknown as feature selection problemthat can significantly contribute to the prediction outcomes. Second, using the output of the first stage, we proposed a stacking model to improve the prediction performances further. Our extensive computations and comparisons across different classifiers show that formulating the feature selection problem as a multi-objective problem instead of a single-objective problem using random forest classifier yields better results. The proposed model will improve the overbooking at clinics, by increasing the patient access to care. We introduced important new features to the literature that can describe the no-show and late cancellation behavior.

Patient No-Show Prediction: A Systematic Literature Review

“…In 2019, Ahmadi et al [ 61 ] used a stacking approach for which the diversity of the model was achieved providing different variables to an RF model. These variables were selected through a genetic algorithm.…”

“…The most used technique within the wrapper methods was stepwise feature selection [ 7 , 32 , 33 , 34 , 38 ]. Other techniques are metaheuristics such as genetic algorithms [ 61 ] or Opposition-Based Self-Adaptive Cohort Intelligence [ 51 ]. Finally, embedded methods incorporate the selection of variables within the model itself.…”

Predictive and Causal Analysis of No-Shows for Medical Exams During COVID-19: A Case Study of Breast Imaging in a Nationwide Israeli Health Organization

“…Therefore, we decided to include in our analysis a single NPI, school closure, which is more easily defined, and serves as an indication for other NPIs. Similar to previous studies 12,13 , we consider late cancellations as no-shows since they do not allow the scheduling system to set an appointment for a new patient. We randomly partitioned the patients in this study into train (56%), validation (14%), and test (30%).…”

Predictors of outpatients’ no-show: big data analytics using apache spark

“…Random Forest performs better than Naïve Bayes and Decision Tree in both stages. NSGA-II3 approaches achieved the highest AUC = 0.697 and lower number of features [18].…”