Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine

Palestinian healthcare institutions face difficulties in providing effective service delivery, particularly in times of crisis. Problems arising from inadequate healthcare service delivery are traceable to issues such as spatial coverage, emergency response time, infrastructure, and manpower. In the...

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Autores principales:	Khaled Yousef Almansi, Abdul Rashid Mohamed Shariff, Ahmad Fikri Abdullah, Sharifah Norkhadijah Syed Ismail
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	GIS site suitability machine learning healthcare Palestine Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/686d9b68cd634704b5c2041981e994c0
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spelling	oai:doaj.org-article:686d9b68cd634704b5c2041981e994c02021-11-25T16:43:27ZHospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine10.3390/app1122110542076-3417https://doaj.org/article/686d9b68cd634704b5c2041981e994c02021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/11054https://doaj.org/toc/2076-3417Palestinian healthcare institutions face difficulties in providing effective service delivery, particularly in times of crisis. Problems arising from inadequate healthcare service delivery are traceable to issues such as spatial coverage, emergency response time, infrastructure, and manpower. In the Gaza Strip, specifically, there is inadequate spatial distribution and accessibility to healthcare facilities due to decades of conflicts. This study focuses on identifying hospital site suitability areas within the Gaza Strip in Palestine. The study aims to find an optimal solution for a suitable hospital location through suitability mapping using relevant environmental, topographic, and geodemographic parameters and their variable criteria. To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. Identification of the most significant parameters (conditioning factors) that influence a suitable hospital location was achieved by employing correlation-based feature selection (CFS) with the search algorithm (greedy stepwise). Thus, the suitability map of potential hospital sites was modeled using a support vector machine (SVM), multilayer perceptron (MLP), and linear regression (LR) models. The results of the predicted sites were validated using CFS cross-validation and the receiver operating characteristic (ROC) curve metrics. The CFS analysis shows very high correlations with R2 values of 0.94, 0. 93, and 0.75 for the SVM, MLP, and LR models, respectively. Moreover, based on areas under the ROC curve, the MLP model produced a prediction accuracy of 84.90%, SVM of 75.60%, and LR of 64.40%. The findings demonstrate that the machine learning techniques used in this study are reliable, and therefore are a promising approach for assessing a suitable location for hospital sites for effective health delivery planning and implementation.Khaled Yousef AlmansiAbdul Rashid Mohamed ShariffAhmad Fikri AbdullahSharifah Norkhadijah Syed IsmailMDPI AGarticleGISsite suitabilitymachine learninghealthcarePalestineTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 11054, p 11054 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	GIS site suitability machine learning healthcare Palestine Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	GIS site suitability machine learning healthcare Palestine Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Khaled Yousef Almansi Abdul Rashid Mohamed Shariff Ahmad Fikri Abdullah Sharifah Norkhadijah Syed Ismail Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine
description	Palestinian healthcare institutions face difficulties in providing effective service delivery, particularly in times of crisis. Problems arising from inadequate healthcare service delivery are traceable to issues such as spatial coverage, emergency response time, infrastructure, and manpower. In the Gaza Strip, specifically, there is inadequate spatial distribution and accessibility to healthcare facilities due to decades of conflicts. This study focuses on identifying hospital site suitability areas within the Gaza Strip in Palestine. The study aims to find an optimal solution for a suitable hospital location through suitability mapping using relevant environmental, topographic, and geodemographic parameters and their variable criteria. To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. Identification of the most significant parameters (conditioning factors) that influence a suitable hospital location was achieved by employing correlation-based feature selection (CFS) with the search algorithm (greedy stepwise). Thus, the suitability map of potential hospital sites was modeled using a support vector machine (SVM), multilayer perceptron (MLP), and linear regression (LR) models. The results of the predicted sites were validated using CFS cross-validation and the receiver operating characteristic (ROC) curve metrics. The CFS analysis shows very high correlations with R2 values of 0.94, 0. 93, and 0.75 for the SVM, MLP, and LR models, respectively. Moreover, based on areas under the ROC curve, the MLP model produced a prediction accuracy of 84.90%, SVM of 75.60%, and LR of 64.40%. The findings demonstrate that the machine learning techniques used in this study are reliable, and therefore are a promising approach for assessing a suitable location for hospital sites for effective health delivery planning and implementation.
format	article
author	Khaled Yousef Almansi Abdul Rashid Mohamed Shariff Ahmad Fikri Abdullah Sharifah Norkhadijah Syed Ismail
author_facet	Khaled Yousef Almansi Abdul Rashid Mohamed Shariff Ahmad Fikri Abdullah Sharifah Norkhadijah Syed Ismail
author_sort	Khaled Yousef Almansi
title	Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine
title_short	Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine
title_full	Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine
title_fullStr	Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine
title_full_unstemmed	Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine
title_sort	hospital site suitability assessment using three machine learning approaches: evidence from the gaza strip in palestine
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/686d9b68cd634704b5c2041981e994c0
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Hospital Site Suitability Assessment Using Three Machine Learning Approaches: Evidence from the Gaza Strip in Palestine

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