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Examining the Impacts of the Built Environment on Quality of Life in Cancer Patients Using Machine Learning

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  • Roya Etminani-Ghasrodashti

    (Center for Transportation Equity, Decisions and Dollars (CTEDD), The University of Texas at Arlington, Arlington, TX 76019, USA)

  • Chen Kan

    (Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA)

  • Muhammad Arif Qaisrani

    (Center for Transportation Equity, Decisions and Dollars (CTEDD), The University of Texas at Arlington, Arlington, TX 76019, USA)

  • Omer Mogultay

    (Department of Mathematics, The University of Texas at Arlington, Arlington, TX 76019-0408, USA)

  • Houliang Zhou

    (Department of Industrial, Manufacturing, and Systems Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA)

Abstract

Despite accumulative evidence regarding the impact of the physical environment on health-related outcomes, very little is known about the relationships between built environment characteristics and the quality of life (QoL) of cancer patients. This study aims to investigate the association between the built environment and QoL by using survey data collected from cancer patients within the United States in 2019. To better understand the associations, we controlled the effects from sociodemographic attributes and health-related factors along with the residential built environment, including density, diversity, design, and distance to transit and hospitals on the self-reported QoL in cancer patients after treatment. Furthermore, machine learning models, i.e., logistic regression, decision tree, random forest, and multilayer perceptron neural network, were employed to evaluate the contribution of these features in predicting the QoL. The results from machine learning models indicated that the travel distance to the closest large hospital, perceived accessibility, distance to transit, and population density were among the most significant predictors of the cancer patients’ QoL. Additionally, the health insurance status, age, and education of patients are associated with QoL. The adverse effects of density on the self-reported QoL in this study can be addressed by individuals’ emotions towards negative aspects of density. Given the strong association between QoL and urban sustainability, consideration should be given to the side effects of urban density on cancer patients’ perceived wellbeing.

Suggested Citation

  • Roya Etminani-Ghasrodashti & Chen Kan & Muhammad Arif Qaisrani & Omer Mogultay & Houliang Zhou, 2021. "Examining the Impacts of the Built Environment on Quality of Life in Cancer Patients Using Machine Learning," Sustainability, MDPI, vol. 13(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5438-:d:553626
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    1. Min Yang & Yuxuan Zou, 2025. "Assessing environmental determinants of subjective well-being via machine learning approaches: a systematic review," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-15, December.

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