Modelling unbalanced catastrophic health expenditure data by using machine‐learning methods

Çınaroğlu, Songül

doi:10.1002/isaf.1483

Cited by 4 publications

(3 citation statements)

References 50 publications

(93 reference statements)

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“…Basic medical insurance does not protect households from catastrophic out-of-pocket (OOP) health expenditures. To reduce inequality, it would be beneficial to utilize big data tools and techniques to effectively screen poor households and strengthen the social and medical aid system for them [ 54 – 57 ].…”

Section: Resultsmentioning

confidence: 99%

The application of artificial intelligence in health financing: a scoping review

Ramezani,

Takian,

Bakhtiari

et al. 2023

Cost Eff Resour Alloc

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Introduction Artificial Intelligence (AI) represents a significant advancement in technology, and it is crucial for policymakers to incorporate AI thinking into policies and to fully explore, analyze and utilize massive data and conduct AI-related policies. AI has the potential to optimize healthcare financing systems. This study provides an overview of the AI application domains in healthcare financing. Method We conducted a scoping review in six steps: formulating research questions, identifying relevant studies by conducting a comprehensive literature search using appropriate keywords, screening titles and abstracts for relevance, reviewing full texts of relevant articles, charting extracted data, and compiling and summarizing findings. Specifically, the research question sought to identify the applications of artificial intelligence in health financing supported by the published literature and explore potential future applications. PubMed, Scopus, and Web of Science databases were searched between 2000 and 2023. Results We discovered that AI has a significant impact on various aspects of health financing, such as governance, revenue raising, pooling, and strategic purchasing. We provide evidence-based recommendations for establishing and improving the health financing system based on AI. Conclusions To ensure that vulnerable groups face minimum challenges and benefit from improved health financing, we urge national and international institutions worldwide to use and adopt AI tools and applications.

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Section: Resultsmentioning

confidence: 99%

The application of artificial intelligence in health financing: a scoping review

Ramezani,

Takian,

Bakhtiari

et al. 2023

Cost Eff Resour Alloc

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“…If the prediction model has achieved an accuracy of 99%, the test results can be considered accurate regardless of whether they are positive or negative. This behavior can cause a problem with highly imbalanced datasets, where the model's accuracy would be almost 100% even without the ability to identify one positive case (Cinaroglu, 2020).…”

Section: Methodsmentioning

confidence: 99%

Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

Papíková

Papík

2022

Intell Sys Acc Fin Mgmt

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Small and medium-sized enterprises are the pillars of an economy, and their poor performance has a negative impact on living standards of population and country development. This study analyzes real-life data of 89,851 small and medium-sized enterprises, out of which 295 have declared bankruptcy. The analysis is performed via 27 financial ratios. The study framework combines seven classifications and three resampling and seven feature selection methods. Out of all classification methods applied, CatBoost has achieved the best results for all combinations of resampling and feature selection methods. CatBoost surpassed the results of other classification methods for the area under curve parameter, achieving a value of 99.95%. The application of resampling methods on different classification models has not identified a statistically significant level of improvement in any of the resampling methods. This finding has also been observed for feature selection methods. Based on these findings, we assume that individual resampling and feature selection methods do not improve model performance compared with the original imbalanced sample's results.Our results suggest that, even though the data sample may be significantly imbalanced with a minority of bankrupt companies, most classification algorithms can handle this imbalance and achieve interesting results. Moreover, our findings provide broad practical application for all stakeholders who could need to detect bankrupting companies.

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“…The explanatory variables were selected from an extensive review of the literature, essentially comprising sociodemographic characteristics [8,12,18,21,25,[52][53][54][55][56][57]. The sociodemographic characteristics are: gender (male, female); age; marital status (married, single, widowed, separated/divorced); educational level (very low: illiterate/primary school incomplete, low: primary or equivalent, medium: secondary school/vocational training, high: university degree or equivalent); activity status (receiving earnings-related pension, employed, unemployed, other situations [housewife, student, etc.…”

Section: Predictor Variablesmentioning

confidence: 99%

The Classification of Profiles of Financial Catastrophe Caused by Out-of-Pocket Payments: A Methodological Approach

2021

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The financial catastrophe resulting from the out-of-pocket payments necessary to access and use healthcare systems has been widely studied in the literature. The aim of this work is to predict the impact of the financial catastrophe a household will face as a result of out-of-pocket payments in long-term care in Spain. These predictions were made using machine learning techniques such as LASSO (Least Absolute Shrinkage and Selection Operator) penalized regression and elastic-net, as well as algorithms like k-nearest neighbors (KNN), MARS (Multivariate Adaptive Regression Splines), random forest, boosted trees and SVM (Support Vector Machine). The results reveal that all the classification methods performed well, with the complex models performing better than the simpler ones and showing no evidence of overfitting. Detecting and defining the profiles of individuals and families most likely to suffer from financial catastrophe is crucial in enabling the design of financial policies aimed at protecting vulnerable groups.

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Modelling unbalanced catastrophic health expenditure data by using machine‐learning methods

Cited by 4 publications

References 50 publications

The application of artificial intelligence in health financing: a scoping review

The application of artificial intelligence in health financing: a scoping review

Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

The Classification of Profiles of Financial Catastrophe Caused by Out-of-Pocket Payments: A Methodological Approach

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