Estimation of service length with the machine learning algorithms and neural networks for patients who receiving home health care

dc.authoridMenteş, Nurettin/0000-0002-5650-4342
dc.authoridCAKMAK, Mehmet Aziz/0000-0002-5040-5642
dc.authorwosidMenteş, Nurettin/HGC-0033-2022
dc.contributor.authorMentes, Nurettin
dc.contributor.authorCakmak, Mehmet Aziz
dc.contributor.authorKurt, Mehmet Emin
dc.date.accessioned2024-08-04T20:53:46Z
dc.date.available2024-08-04T20:53:46Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe main purpose of the study is to develop an estimation model using machine learning algorithms and to ensure the effective and efficient implementation of home health care service planning in hospitals with these algorithms. The necessary approvals for the study were obtained. The data set was created by obtaining patient data (except for data such as Turkish Republic identification number) from 14 hospitals providing Home Health Care Services in the city of Diyarbakir. The data set was subjected to necessary pre-processing and descriptive statistics were applied. For the estimation model, Decision Tree, Random Forest and Multi-layer Perceptron Neural Network algorithms were used. It was found that the number of days of home health care service, which the patients received, varied depending on their age and gender. It was observed that the patients were generally in the disease groups that required Physiotherapy and Rehabilitation treatments. It was determined that the length of service for patients can be predicted with a high reliability rate (Multi-Layer Model Acc: 90.4%, Decision Tree Model Acc: 86.4%, Random Forest Model Acc: 88.5%) using machine learning algorithms. In the light of the findings and data patterns obtained in the study, it is thought that effective and efficient planning will be made in terms of health management. In addition, it is believed that estimating the average length of service for patients will contribute to strategic planning of human resources for health, and to reducing medical consumables, drugs and hospital expenses.en_US
dc.identifier.doi10.1016/j.evalprogplan.2023.102324
dc.identifier.issn0149-7189
dc.identifier.issn1873-7870
dc.identifier.pmid37290209en_US
dc.identifier.scopus2-s2.0-85161307702en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.evalprogplan.2023.102324
dc.identifier.urihttps://hdl.handle.net/11616/101390
dc.identifier.volume100en_US
dc.identifier.wosWOS:001015344600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEvaluation and Program Planningen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine Learning Algorithmsen_US
dc.subjectHome Health Care Servicesen_US
dc.subjectEstimation of Length of Serviceen_US
dc.titleEstimation of service length with the machine learning algorithms and neural networks for patients who receiving home health careen_US
dc.typeArticleen_US

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