IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0051285.html
   My bibliography  Save this article

Comparisons of Prediction Models of Quality of Life after Laparoscopic Cholecystectomy: A Longitudinal Prospective Study

Author

Listed:
  • Hon-Yi Shi
  • Hao-Hsien Lee
  • Jinn-Tsong Tsai
  • Wen-Hsien Ho
  • Chieh-Fan Chen
  • King-Teh Lee
  • Chong-Chi Chiu

Abstract

Background: Few studies of laparoscopic cholecystectomy (LC) outcome have used longitudinal data for more than two years. Moreover, no studies have considered group differences in factors other than outcome such as age and nonsurgical treatment. Additionally, almost all published articles agree that the essential issue of the internal validity (reproducibility) of the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR) and multiple linear regression (MLR) models has not been adequately addressed. This study proposed to validate the use of these models for predicting quality of life (QOL) after LC and to compare the predictive capability of ANNs with that of SVM, GPR and MLR. Methodology/Principal Findings: A total of 400 LC patients completed the SF-36 and the Gastrointestinal Quality of Life Index at baseline and at 2 years postoperatively. The criteria for evaluating the accuracy of the system models were mean square error (MSE) and mean absolute percentage error (MAPE). A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the variables in order of importance. Compared to SVM, GPR and MLR models, the ANN model generally had smaller MSE and MAPE values in the training data set and test data set. Most ANN models had MAPE values ranging from 4.20% to 8.60%, and most had high prediction accuracy. The global sensitivity analysis also showed that preoperative functional status was the best parameter for predicting QOL after LC. Conclusions/Significance: Compared with SVM, GPR and MLR models, the ANN model in this study was more accurate in predicting patient-reported QOL and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.

Suggested Citation

  • Hon-Yi Shi & Hao-Hsien Lee & Jinn-Tsong Tsai & Wen-Hsien Ho & Chieh-Fan Chen & King-Teh Lee & Chong-Chi Chiu, 2012. "Comparisons of Prediction Models of Quality of Life after Laparoscopic Cholecystectomy: A Longitudinal Prospective Study," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.
  • Handle: RePEc:plo:pone00:0051285
    DOI: 10.1371/journal.pone.0051285
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0051285
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0051285&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0051285?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. MinFang Tao & YinCheng Teng & HongFang Shao & Ping Wu & Edward J Mills, 2011. "Knowledge, Perceptions and Information about Hormone Therapy (HT) among Menopausal Women: A Systematic Review and Meta-Synthesis," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-10, September.
    2. Paolo Piaggi & Chita Lippi & Paola Fierabracci & Margherita Maffei & Alba Calderone & Mauro Mauri & Marco Anselmino & Giovanni Battista Cassano & Paolo Vitti & Aldo Pinchera & Alberto Landi & Ferrucci, 2010. "Artificial Neural Networks in the Outcome Prediction of Adjustable Gastric Banding in Obese Women," PLOS ONE, Public Library of Science, vol. 5(10), pages 1-12, October.
    3. Dayle L Sampson & Tony J Parker & Zee Upton & Cameron P Hurst, 2011. "A Comparison of Methods for Classifying Clinical Samples Based on Proteomics Data: A Case Study for Statistical and Machine Learning Approaches," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-11, September.
    4. Andrea Lancichinetti & Filippo Radicchi & José J Ramasco & Santo Fortunato, 2011. "Finding Statistically Significant Communities in Networks," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-18, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Satwant Kumar & Madhu Lata Rana & Khushboo Verma & Narayanjeet Singh & Anil Kumar Sharma & Arun Kumar Maria & Gobind Singh Dhaliwal & Harkiran Kaur Khaira & Sunil Saini, 2014. "PrediQt-Cx: Post Treatment Health Related Quality of Life Prediction Model for Cervical Cancer Patients," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-8, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Jianshe & Zhang, Long & Li, Yong & Jiao, Yang, 2016. "Partition signed social networks via clustering dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 568-582.
    2. Gregory, Steve, 2012. "Ordered community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2752-2763.
    3. Jin-Xing Liu & Yong Xu & Chun-Hou Zheng & Yi Wang & Jing-Yu Yang, 2012. "Characteristic Gene Selection via Weighting Principal Components by Singular Values," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.
    4. Wu, Zhihao & Lin, Youfang & Wan, Huaiyu & Tian, Shengfeng & Hu, Keyun, 2012. "Efficient overlapping community detection in huge real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2475-2490.
    5. Greg Morrison & L Mahadevan, 2012. "Discovering Communities through Friendship," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    6. Jiang, Yawen & Jia, Caiyan & Yu, Jian, 2013. "An efficient community detection method based on rank centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2182-2194.
    7. Franke, R., 2016. "CHIMERA: Top-down model for hierarchical, overlapping and directed cluster structures in directed and weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 384-408.
    8. Chagas, Guilherme Oliveira & Lorena, Luiz Antonio Nogueira & dos Santos, Rafael Duarte Coelho, 2022. "A hybrid heuristic for overlapping community detection through the conductance minimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    9. Dugué, Nicolas & Perez, Anthony, 2022. "Direction matters in complex networks: A theoretical and applied study for greedy modularity optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    10. Badie, Reza & Aleahmad, Abolfazl & Asadpour, Masoud & Rahgozar, Maseud, 2013. "An efficient agent-based algorithm for overlapping community detection using nodes’ closeness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5231-5247.
    11. Klapka, Pavel & Kraft, Stanislav & Halás, Marián, 2020. "Network based definition of functional regions: A graph theory approach for spatial distribution of traffic flows," Journal of Transport Geography, Elsevier, vol. 88(C).
    12. Wang, Yuyao & Bu, Zhan & Yang, Huan & Li, Hui-Jia & Cao, Jie, 2021. "An effective and scalable overlapping community detection approach: Integrating social identity model and game theory," Applied Mathematics and Computation, Elsevier, vol. 390(C).
    13. Fu, Xianghua & Liu, Liandong & Wang, Chao, 2013. "Detection of community overlap according to belief propagation and conflict," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 941-952.
    14. Dafne E. van Kuppevelt & Rena Bakhshi & Eelke M. Heemskerk & Frank W. Takes, 2022. "Community membership consistency applied to corporate board interlock networks," Journal of Computational Social Science, Springer, vol. 5(1), pages 841-860, May.
    15. Jiang, Zhongzhou & Liu, Jing & Wang, Shuai, 2016. "Traveling salesman problems with PageRank Distance on complex networks reveal community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 293-302.
    16. Kyle F Davis & Paolo D'Odorico & Francesco Laio & Luca Ridolfi, 2013. "Global Spatio-Temporal Patterns in Human Migration: A Complex Network Perspective," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-8, January.
    17. Lovro Šubelj & Nees Jan van Eck & Ludo Waltman, 2016. "Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-23, April.
    18. Akshat Singhal & Song Cao & Christopher Churas & Dexter Pratt & Santo Fortunato & Fan Zheng & Trey Ideker, 2020. "Multiscale community detection in Cytoscape," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-10, October.
    19. Hao Wu & Lin Gao & Jihua Dong & Xiaofei Yang, 2014. "Detecting Overlapping Protein Complexes by Rough-Fuzzy Clustering in Protein-Protein Interaction Networks," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-13, March.
    20. Wang, Tai-Chi & Phoa, Frederick Kin Hing, 2016. "A scanning method for detecting clustering pattern of both attribute and structure in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 295-309.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0051285. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.