2023
DOI: 10.1371/journal.pone.0279540
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The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data

Abstract: Our aim was to predict future high-cost patients with machine learning using healthcare claims data. We applied a random forest (RF), a gradient boosting machine (GBM), an artificial neural network (ANN) and a logistic regression (LR) to predict high-cost patients in the following year. Therefore, we exploited routinely collected sickness funds claims and cost data of the years 2016, 2017 and 2018. Various specifications of each algorithm were trained and cross-validated on training data (n = 20,984) with clai… Show more

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Cited by 11 publications
(1 citation statement)
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“…Compared to other machine learning models, the decision tree has the following advantages: (1) it can be visualized and is simple to understand and interpret in clinical practice; (2) it provides a remarkably transparent decision‐making process, allowing deep exploration of features; and (3) due to its high transparency, the decision‐making process can be easily validated by an expert that greatly enhances its utility in situations containing high uncertainty (Amendolara et al., 2023 ; Bae, 2014 ; Plante et al., 1986 ; Podgorelec et al., 2002 ; Ting Sim et al., 2023 ). A growing body of literature has demonstrated the effectiveness of decision trees in predicting the occurrence as well as the prognosis of health‐related outcomes (Toyoda et al., 2023 ; Yang et al., 2021 ; Zhou et al., 2023 ), and some studies directly compared decision tree model with common machine learning models to solve prediction problems (Hu et al., 2022 ; Langenberger et al., 2023 ). Previous work on comparison of logistic regression and decision tree models found comparable predictive values (Wentzlof et al., 2019 ; Zhang et al., 2022 ).…”
Section: Introductionmentioning
confidence: 99%
“…Compared to other machine learning models, the decision tree has the following advantages: (1) it can be visualized and is simple to understand and interpret in clinical practice; (2) it provides a remarkably transparent decision‐making process, allowing deep exploration of features; and (3) due to its high transparency, the decision‐making process can be easily validated by an expert that greatly enhances its utility in situations containing high uncertainty (Amendolara et al., 2023 ; Bae, 2014 ; Plante et al., 1986 ; Podgorelec et al., 2002 ; Ting Sim et al., 2023 ). A growing body of literature has demonstrated the effectiveness of decision trees in predicting the occurrence as well as the prognosis of health‐related outcomes (Toyoda et al., 2023 ; Yang et al., 2021 ; Zhou et al., 2023 ), and some studies directly compared decision tree model with common machine learning models to solve prediction problems (Hu et al., 2022 ; Langenberger et al., 2023 ). Previous work on comparison of logistic regression and decision tree models found comparable predictive values (Wentzlof et al., 2019 ; Zhang et al., 2022 ).…”
Section: Introductionmentioning
confidence: 99%