Predictors of Outpatients’ No-Show: Big Data Analytics using Apache Spark

Daghistani, Tahani; AlGhamdi, Huda; Alshammari, Riyad; AlHazme, Raed H.

doi:10.21203/rs.3.rs-33216/v1

2020

DOI: 10.21203/rs.3.rs-33216/v1

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Predictors of Outpatients’ No-Show: Big Data Analytics using Apache Spark

Tahani Daghistani¹,

Huda AlGhamdi²,

Riyad Alshammari

et al.

Abstract: Outpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in Spark platform. The aim of this paper is exploring factors that affec… Show more

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2024

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Decision analysis framework for predicting no-shows to appointments using machine learning algorithms

Deina,

Fogliatto,

da Silveira

et al. 2024

BMC Health Serv Res

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Background No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources. Methods In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets. The framework includes a novel use of z-fold cross-validation performed twice during the modeling process to improve model robustness and generalization. We also introduce Symbolic Regression (SR) as a classification algorithm and Instance Hardness Threshold (IHT) as a resampling technique and compared their performance with that of other classification algorithms, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), and resampling techniques, such as Random under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE) and NearMiss-1. We validated the framework using two attendance datasets from Brazilian hospitals with no-show rates of 6.65% and 19.03%. Results From the academic perspective, our study is the first to propose using SR and IHT to predict the no-show of patients. Our findings indicate that SR and IHT presented superior performances compared to other techniques, particularly IHT, which excelled when combined with all classification algorithms and led to low variability in performance metrics results. Our results also outperformed sensitivity outcomes reported in the literature, with values above 0.94 for both datasets. Conclusion This is the first study to use SR and IHT methods to predict patient no-shows and the first to propose performing z-fold cross-validation twice. Our study highlights the importance of avoiding relying on few validation runs for imbalanced datasets as it may lead to biased results and inadequate analysis of the generalization and stability of the models obtained during the training stage.

show abstract

Decision analysis framework for predicting no-shows to appointments using machine learning algorithms

Deina,

Fogliatto,

da Silveira

et al. 2024

BMC Health Serv Res

View full text Add to dashboard Cite

show abstract

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Predictors of Outpatients’ No-Show: Big Data Analytics using Apache Spark

Cited by 1 publication

References 18 publications

Decision analysis framework for predicting no-shows to appointments using machine learning algorithms

Decision analysis framework for predicting no-shows to appointments using machine learning algorithms

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