Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors

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dc.authoridKurutkan, Mehmet Nurullah/0000-0002-3740-4231;
dc.contributor.authorOrhan, Fatih
dc.contributor.authorKurutkan, Mehmet Nurullah
dc.date.accessioned2025-10-11T20:48:05Z
dc.date.available2025-10-11T20:48:05Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractObjectivePredicting healthcare demand is essential for effective resource allocation and planning. This study applies Andersen's Behavioral Model of Health Services Use, focusing on predisposing, enabling, and need factors, using data from the 2022 Turkey Health Survey by TUIK. Machine learning methods provide a powerful approach to analyze these factors and their combined impact on healthcare utilization, offering valuable insights for health policy.MethodsSeven different machine learning models-Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, XGBoost, and Gradient Boosting-were utilized. Feature selection was conducted to identify the most significant factors influencing healthcare demand. The models were evaluated for accuracy and generalization ability using performance metrics such as recall, precision, F1 score, and ROC AUC.ResultsThe study identified key features affecting healthcare demand. For predisposing factors, gender, educational level, and age group were significant. Enabling factors included treatment costs, community interest, and payment difficulties. Need factors were influenced by smoking status, chronic diseases, and overall health status. The models demonstrated high recall (approximately 0.90) and strong F1 scores (ranging from 0.87 to 0.88), indicating a balanced performance between precision and recall. Among the models, Gradient Boosting, XGBoost, and Logistic Regression consistently outperformed others, achieving the highest predictive accuracy. Random Forest and SVM also performed well, showing robust classification capability.ConclusionsThe findings highlight the effectiveness of machine learning methods in predicting healthcare demand, providing valuable insights for health policy and resource allocation. Gradient Boosting, XGBoost, and Logistic Regression emerged as the most reliable models, demonstrating superior generalization and classification performance. Understanding the separate and combined effects of predisposing, enabling, and need factors on healthcare demand can contribute to more efficient and data-driven healthcare planning, facilitating strategic decision-making in resource allocation and service delivery.en_US
dc.identifier.doi10.1186/s12913-025-12502-5
dc.identifier.issn1472-6963
dc.identifier.issue1en_US
dc.identifier.pmid40075408en_US
dc.identifier.scopus2-s2.0-105000062524en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1186/s12913-025-12502-5
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21744
dc.identifier.volume25en_US
dc.identifier.wosWOS:001443426200004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherBmcen_US
dc.relation.ispartofBmc Health Services Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectHealthcare demanden_US
dc.subjectAndersen behavioral modelen_US
dc.subjectMachine learningen_US
dc.subjectFeature selectionen_US
dc.subjectHealth services utilizationen_US
dc.subjectPredictive modelingen_US
dc.titlePredicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factorsen_US
dc.typeArticleen_US

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