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Causal Modeling Between Factors on Quality of Life in Cancer Patients Using S3C-Latent Algorithm

Journal of Information Systems Engineering and Business Intelligence · Vol. 7 No. 1 (2021) · hlm. 74–83

DOI
10.20473/jisebi.7.1.74-83
ISSN
2443-2555, 2598-6333
Terbit
2021-04-27
Penerbit
Universitas Airlangga
Bahasa
eng

Abstrak

Background: Cancer patients can experience both physical and non-physical problems such as psychosocial, spiritual, and emotional problems, which impact the quality of life. Previous studies on quality of life mostly have employed multivariate analyses. To our knowledge, no studies have focused yet on the underlying causal relationship between factors representing the quality of life of cancer patients, which is very important when attempting to improve the quality of life. Objective: The study aims to model the causal relationships between the factors that represent cancer and quality of life.Methods: This study uses the S3C-Latent method to estimate the causal model relationships between the factors. The S3C-Latent method combines Structural Equation Model (SEM), a multi objective optimization method, and the stability selection approach, to estimate a stable and parsimonious causal model.Results: There are nine causal relations that have been found, i.e., from physical to global health with a reliability score of 0.73, to performance status with a reliability score of 1, from emotional to global health with a reliability score of 0.71, to performance status with a reliability score of 0.82, from nausea, loss of appetite, dyspnea, insomnia, loss of appetite and from constipation to performance status with reliability scores of 0.76; 1; 0.61; 0.76; 0.72; 0.70, respectively. Moreover, this study found that 15 associations (strong relation where the causal direction cannot be determined from the data alone) between factors with reliability scores range from 0.65 to 1.Conclusion: The estimated model is consistent with the results shown in previous studies. The model is expected to provide evidence-based recommendation for health care providers in designing strategies to increase cancer patients' life quality. For future research, we suggest studies to include more variables in the model to capture a broader view to the problem.

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  • Universitas Gadjah Mada
  • Institut Teknologi Telkom Purwokerto
  • Islamic University of Indonesia
  • Hasanuddin University
  • University of Nigeria
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Rekan penulisOrganisasiArtikel bersama
Nur ChayatiAfiliasi tidak diketahui2
Ismail SetyopranotoAfiliasi tidak diketahui2
Yuan Sa'adatiIslamic University of Indonesia2
Rizki Surtiyan SuryaIslamic University of Indonesia2
Siti KhomsahInstitut Teknologi Telkom Purwokerto1
Mohammad Teddy SyamkallaInstitut Teknologi Telkom Purwokerto1
Andina SetyawatiHasanuddin University1
Mae Sri Hartati WahyuningsihUniversitas Gadjah Mada1
Dwi Aris Agung NugrahaningsihUniversitas Gadjah Mada1
Sam Chidi IbenemeUniversity of Nigeria1
Nyoman Sri SubawaAfiliasi tidak diketahui1
Marselus YansenAfiliasi tidak diketahui1
Anak Agung Made WirajayaAfiliasi tidak diketahui1
AKICHIKA MIKAMIAfiliasi tidak diketahui1
Heny Suseani PangastutiAfiliasi tidak diketahui1
Eriyono Budi WijoyoAfiliasi tidak diketahui1
Shinta Restu WibawaAfiliasi tidak diketahui1
Ami Novianti SubagyaAfiliasi tidak diketahui1
Aisya Puteri AndariAfiliasi tidak diketahui1
Dwi AprilianaAfiliasi tidak diketahui1
Qurrota Aini Zahrotu SholikhahAfiliasi tidak diketahui1
Haryani HaryaniAfiliasi tidak diketahui1
Catur KurniawanAfiliasi tidak diketahui1
Syahirul AlimAfiliasi tidak diketahui1
Sri SetiyariniUniversitas Gadjah Mada1
Rahmah Yanita KusumaAfiliasi tidak diketahui1
Ariani Arista Putri PertiwiAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JISEBI/oai, pertama di-harvest 29 Okt 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Causal Modeling Between Factors on Quality of Life in Cancer Patients Using S3C-Latent Algorithm</dc:title>
	<dc:creator>Nur, Yohani Setiya Rafika</dc:creator>
	<dc:creator>Rahmadi, Ridho</dc:creator>
	<dc:creator>Effendy, Christantie</dc:creator>
	<dc:description xml:lang="en-US">Background: Cancer patients can experience both physical and non-physical problems such as psychosocial, spiritual, and emotional problems, which impact the quality of life. Previous studies on quality of life mostly have employed multivariate analyses. To our knowledge, no studies have focused yet on the underlying causal relationship between factors representing the quality of life of cancer patients, which is very important when attempting to improve the quality of life.  Objective: The study aims to model the causal relationships between the factors that represent cancer and quality of life.Methods: This study uses the S3C-Latent method to estimate the causal model relationships between the factors. The S3C-Latent method combines Structural Equation Model (SEM), a multi objective optimization method, and the stability selection approach, to estimate a stable and parsimonious causal model.Results: There are nine causal relations that have been found, i.e., from physical to global health with a reliability score of 0.73, to performance status with a reliability score of 1, from emotional to global health with a reliability score of 0.71, to performance status with a reliability score of 0.82, from nausea, loss of appetite, dyspnea, insomnia, loss of appetite and from constipation to performance status with reliability scores of 0.76; 1; 0.61; 0.76; 0.72; 0.70, respectively. Moreover, this study found that 15 associations (strong relation where the causal direction cannot be determined from the data alone) between factors with reliability scores range from 0.65 to 1.Conclusion: The estimated model is consistent with the results shown in previous studies. The model is expected to provide evidence-based recommendation for health care providers in designing strategies to increase cancer patients' life quality. For future research, we suggest studies to include more variables in the model to capture a broader view to the problem.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2021-04-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/25685</dc:identifier>
	<dc:identifier>10.20473/jisebi.7.1.74-83</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 7 No. 1 (2021): April; 74-83</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/25685/13995</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 The Authors. Published by Universitas Airlangga.</dc:rights>
</oai_dc:dc>