Performance Evaluation of Hospital Site Suitability Using Multilayer Perceptron (MLP) and Analytical Hierarchy Process (AHP) Models in Malacca, Malaysia

Khaled Yousef Almansi, Abdul Rashid Mohamed Shariff, Bahareh Kalantar, Ahmad Fikri Abdullah, Sharifah Norkhadijah Syed Ismail and Naonori Ueda
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Khaled Yousef Almansi: Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia
Abdul Rashid Mohamed Shariff: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia
Bahareh Kalantar: RIKEN Center of Advanced Intelligence Project, The Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan
Ahmad Fikri Abdullah: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia
Sharifah Norkhadijah Syed Ismail: Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia
Naonori Ueda: RIKEN Center of Advanced Intelligence Project, The Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan

Sustainability, 2022, vol. 14, issue 7, 1-36

Abstract: This study focuses on suitable site identification for constructing a hospital in Malacca, Malaysia. Using significant environmental, topographic, and geodemographic factors, the study evaluated and compared machine learning (ML) and multicriteria decision analysis (MCDA) for hospital site suitability mapping to discover the highest influential factors that minimize the error ratio and maximize the effectiveness of the suitability investigation. Identification of the most significant conditioning parameters that impact the choice of an appropriate hospital site was accomplished using correlation-based feature selection (CFS) with a search algorithm (greedy stepwise). To model the potential hospital site map, we utilized multilayer perceptron (MLP) and analytical hierarchy process (AHP) models. The outcome of the predicted site models was validated utilizing CFS 10-fold cross-validation, as well as ROC curve (receiver operating characteristic curve). The analysis of CFS indicated a very high correlation with R2 values of 0.99 for the MLP model. However, the ROC curve indicated a prediction accuracy of 80% for the MLP model and 83% for the AHP model. The findings revealed that the MLP model is reliable and consistent with the AHP. It is a sufficiently promising approach to the location suitability of hospitals to ensure effective planning and performance of healthcare delivery.

Keywords: GIS; hospital site suitability; multilayer perception (MLP); analytical hierarchy process (AHP) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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