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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Khaled Yousef Almansi & Abdul Rashid Mohamed Shariff & Bahareh Kalantar & Ahmad Fikri Abdullah & Sharifah Norkhadijah Syed Ismail & Naonori Ueda, 2022. "Performance Evaluation of Hospital Site Suitability Using Multilayer Perceptron (MLP) and Analytical Hierarchy Process (AHP) Models in Malacca, Malaysia," Sustainability, MDPI, vol. 14(7), pages 1-36, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3731-:d:776854
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    References listed on IDEAS

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