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Decision Tree-Based Transdisciplinary Systems Modelling for Cognitive Status in Neurological Diseases

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

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Abstract

This paper addresses the concept of an up-to-date transdisciplinary system modelling based on decision tree within the framework of systems theory. Systems theory constructs effective models for the analysis of complex systems since this comprehensive theory is capable of providing links between the problems and dynamics of systems. Particularly, for the complex and challenging environments, the solutions to the problems can be managed more effectively based on a systems approach. Neurological diseases concern the brain which has a complex structure and dynamics. Being equipped with the accurate medical knowledge plays a critical role in tackling these neurological problems. The interconnected relationships require a carefully-characterized transdisciplinary approach integrating systems conduct and mathematical modelling. Effective solutions lie in cognitive status, namely awareness and a satisfactory level of health knowledge. Within this framework, this study aims at revealing the lack of required general and medical health knowledge on neurological diseases (Alzheimer’s, dementia, Parkinson’s, stroke, epilepsy and migraine) among individuals. For this purpose, an online survey was conducted on 381 respondents, through which awareness on medical knowledge and general health knowledge were assessed for each disease. The following approaches (methods) were applied: firstly, rule-based decision tree algorithm was applied since its structure enables the interpretation of the data and works effectively with feature computations. Subsequently, statistical analyses were performed. The decision tree analyses and statistical analyses reveal parallel results with one another, which demonstrate that when compared with the knowledge of elder people, the knowledge of young population is limited in general and medical health knowledge. Compared with previous works, no related work exists in the literature where a transdisciplinary approach with these proposed methods are used. The experimental results demonstrate the significant difference between medical knowledge and general health knowledge among individuals depending on different attributes. The study attempts to reveal a new approach for dealing with diseases, developing positive attitudes besides establishing effective long-term behavioural patterns and strategies based on required knowledge and mindfulness.

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References

  1. Wognum, N., Verhagen, W.J., Stjepandı\(\acute{c}\), J.: Trans-disciplinary systems as complex systems. In: Transdisciplinary Engineering: A Paradigm Shift-Proceedings of the 24th ISPE Inc., International Conference on Transdisciplinary Engineering, vol. 5. IOS Press (2017)

    Google Scholar 

  2. Jackson, J., Ware, C., Churchyard, R., Hanseeuw, B.: Interdisciplinary and transdisciplinary perspectives: on the road to a holistic approach to dementia prevention and care. J. Alzheimer’s Dis. Rep., 1–10 (Preprint)

    Google Scholar 

  3. Davenport, T., Kalakota, R.: The potential for artificial intelligence in healthcare. Future Healthc. J. 6(2), 94 (2019)

    Article  Google Scholar 

  4. Tan, J.P., et al.: Awareness status of chronic disabling neurological diseases among elderly veterans. Chin. Med. J. 128(10), 1293 (2015)

    Article  Google Scholar 

  5. Siuly, S., Zhang, Y.: Medical big data: neurological diseases diagnosis through medical data analysis. Data Sci. Eng. 1(2), 54–64 (2016)

    Article  Google Scholar 

  6. Pais, M., Martinez, L., Ribeiro, O., Loureiro, J., Fernandez, R., Valiengo, L., Forlenza, O.V.: Early diagnosis and treatment of Alzheimer’s disease: new definitions and challenges. Brazilian J. Psychiatry (AHEAD) 42(4), 431–441 (2020)

    Article  Google Scholar 

  7. Nestor, P.J., Scheltens, P., Hodges, J.R.: Advances in the early detection of Alzheimer’s disease. Nat. Med. 10(7), S34–S41 (2004)

    Article  Google Scholar 

  8. Karaca, Y., Moonis, M., Siddiqi, A.H., Turan, B.: Gini based learning for the classification of alzheimer’s disease and features identification with automatic RGB segmentation algorithm. In: Gervasi, O., et al. (eds.) ICCSA 2018. LNCS, vol. 10961, pp. 92–106. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95165-2_7

    Chapter  Google Scholar 

  9. Brandt, T., Caplan, L.R., Dichgans, J., Diener, H.C., Kennard, C.: Neurological Disorders: Course and Treatment. Gulf Professional Publishing, Houstan (2003)

    Google Scholar 

  10. Weiner, W.J., Shulman, L.M., Lang, A.E.: Parkinson’s Disease: A Complete Guide for Patients and Families. JHU Press, Baltimore (2013)

    Google Scholar 

  11. Mendelow, A.D., Lo, E.H., Sacco, R.L., Faan, M.M.F., Wong, L.K.: Stroke: Pathophysiology, Diagnosis, and Management. Elsevier Health Sciences, Amsterdam (2015)

    Google Scholar 

  12. Karaca, Y., Cattani, C., Moonis, M., Bayrak, Ş.: Stroke subtype clustering by multifractal bayesian denoising with fuzzy C means and K-means algorithms. Complexity 2018, 15 Pages (2018). Article ID 9034647

    Article  Google Scholar 

  13. Shorvon, S., Guerrini, R., Cook, M., Lhatoo, S.: Oxford Textbook of Epilepsy and Epileptic Seizures. OUP, Oxford (2012)

    Book  Google Scholar 

  14. Diamond, S., Cady, R.K., Diamond, M.L., Green, M.W., Martin, V.T.: Headache and Migraine Biology and Management. Academic Press, Cambridge (2015)

    Google Scholar 

  15. Good, D.C.: Episodic Neurologic Symptoms. In: Clinical Methods: The History, Physical, and Laboratory Examinations, 3rd edn. Butterworths (1990)

    Google Scholar 

  16. Bishop, S.R., et al.: Mindfulness: a proposed operational definition. Clin. Psych. 11, 230–241 (2004)

    Google Scholar 

  17. Hudlicka, E.: Virtual training and coaching of health behavior: example from mindfulness meditation training. Patient Educ. Couns. 92(2), 160–166 (2013)

    Article  Google Scholar 

  18. Camerini, L., Camerini, A.L., Schulz, P.J.: Do participation and personalization matter? A model-driven evaluation of an Internet-based patient education intervention for fibromyalgia patients. Patient Educ. Couns. 92(2), 229–234 (2013)

    Article  Google Scholar 

  19. Nutbeam, D.: Health promotion glossary. Health Promotion Int. 13(4), 357 (1998)

    Article  Google Scholar 

  20. Rutter, D., Quine, L.: Social cognition models and changing health behaviours. In: Rutter, D., Quine, L. (eds.) Changing Health Behaviour: Intervention and Research with Social Cognition Models, pp. 1–27. Open University Press, London (2002)

    Google Scholar 

  21. Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991)

    Article  Google Scholar 

  22. Becker, M.H.: The health belief model and sick role behavior. Health Educ. Monogr. 2(4), 409–419 (1974)

    Article  Google Scholar 

  23. Becker, M.H., Maiman, L.A., Kirscht, J.P., Haefner, D.P., Drachman, R.H.: The health belief model and prediction of dietary compliance: a field experiment. J. Health Soc. Behav. 18, 348–366 (1977)

    Article  Google Scholar 

  24. Abraham, C., Sheeran, P.: The health belief model. In: Ayers, S., Baum, A., McManus, C., Newman, S., Wallston, K., Weinman, J., et al. (eds.) Cambridge Handbook of Psychology, Health and Medicine Cambridge, pp. 97–102. Cambridge University Press, Cambridge (2007)

    Google Scholar 

  25. Bian, J., Guo, Y., He, Z., Hu, X. (eds.): Social Web and Health Research: Benefits, Limitations, and Best Practices. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14714-3

    Book  Google Scholar 

  26. Star, L., Moghadas, S.M.: The role of mathematical modelling in public health planning and decision making. Purple Paper, National Collaborative Center for Infectious Diseases (2010)

    Google Scholar 

  27. Cassidy, R., Singh, N.S., Schiratti, P.R., Semwanga, A., Binyaruka, P., Sachingongu, N., Blanchet, K.: Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models. BMC Health Serv. Res. 19(1), 845 (2019)

    Article  Google Scholar 

  28. Njeuhmeli, E., et al.: Using mathematical modeling to inform health policy: a case study from voluntary medical male circumcision scale-up in eastern and southern Africa and proposed framework for success. PLoS ONE 14(3), e0213605 (2019)

    Article  Google Scholar 

  29. Kreps, G.L., Neuhauser, L.: Artificial intelligence and immediacy: designing health communication to personally engage consumers and providers. Patient Educ. Couns. 92(2), 205–210 (2013)

    Article  Google Scholar 

  30. Brailsford, S.C., Harper, P.R., Patel, B., Pitt, M.: An analysis of the academic literature on simulation and modelling in health care. J. Simul. 3(3), 130–140 (2009)

    Article  Google Scholar 

  31. Fan, H., He, J.: Knowledge base construction based on knowledge fusion process model. In: Chen, H., Zeng, D., Yan, X., Xing, C. (eds.) ICSH 2019. LNCS, vol. 11924, pp. 333–344. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34482-5_30

    Chapter  Google Scholar 

  32. Brian, D., Carpenter, S.B.: The alzheimer’s disease knowledge scale: development and psychometric properties. Gerontologist 49(2), 236–247 (2009)

    Article  Google Scholar 

  33. Annear, M.J., et al.: Dementia knowledge assessment scale: development and preliminary psychometric properties. J. Am. Geriatr. Soc. 63(11), 2375–81 (2015)

    Article  Google Scholar 

  34. O’Donoghue, N.F., Duncan, J.S., Sander, J.W.A.S.: The National Hospital Seizure Severity Scale: a further development of the Chalfont Seizure Severity Scale. Epilepsia 37, 563–71 (1996)

    Article  Google Scholar 

  35. Stewart, W.F.: Reliability of the migraine disability assessment score in a population-based sample of headache sufferers. Cephalalgia 19(2), 107–114 (1999)

    Article  Google Scholar 

  36. Ware, J.E.: Jr practical implications of item response theory and computerized adaptive testing: a brief summary of ongoing studies of widely used headache impact scales. Med. Care 38(9), 1173–1182 (2000)

    Google Scholar 

  37. Centers for Disease Control and Prevention 2011. Behavioral risk factor surveillance system questionnaire (2011)

    Google Scholar 

  38. National Institute of Neurological Disorders and Stroke (NINDS). Stroke information page (2013)

    Google Scholar 

  39. Meena, K., Tayal, D.K., Gupta, V., Fatima, A.: Using classification techniques for statistical analysis of Anemia. Artif. Intell. Med. 94, 138–152 (2019)

    Article  Google Scholar 

  40. Ghiasi, M.M., Zendehboudi, S., Mohsenipour, A.A.: Decision tree-based diagnosis of coronary artery disease: CART model. Comput. Methods Programs Biomed. 192, 105400 (2020)

    Article  Google Scholar 

  41. Ghiasi, M.M., Zendehboudi, S.: Decision tree-based methodology to select a proper approach for wart treatment. Comput. Biol. Med. 108, 400–409 (2019)

    Article  Google Scholar 

  42. Karaca, Y., Cattani, C.: Computational Methods for Data Analysis. Walter de Gruyter GmbH, Berlin (2018). 978–3110496352

    MATH  Google Scholar 

  43. González-Rodríguez, G., Colubi, A., Gil, M.Á.: Fuzzy data treated as functional data: a one-way ANOVA test approach. Comput. Stat. Data Anal. 56(4), 943–955 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  44. Jung, Y., Hu, J.: Reversed low-rank ANOVA model for transforming high dimensional genetic data into low dimension. J. Korean Stat. Soc. 48(2), 169–178 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  45. De Haan, J.R., et al.: Robust ANOVA for microarray data. Chemometr. Intell. Lab. Syst. 98(1), 38–44 (2009)

    Article  Google Scholar 

  46. Rutherford, A.: ANOVA and ANCOVA: A GLM Approach. Wiley, Hoboken (2011)

    Book  MATH  Google Scholar 

  47. Girden, E.R.: ANOVA: repeated measures, vol. 84 (1992)

    Google Scholar 

  48. Simecek, P., Simecek, M.: Modification of Tukey’s additivity test. J. Stat. Plann. Infer. 143(1), 197–201 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  49. Driscoll, W.C.: Robustness of the ANOVA and Tukey-Kramer statistical tests. Comput. Ind. Eng. 31(1–2), 265–268 (1996)

    Article  Google Scholar 

  50. Mackridge, A., Rowe, P.: A Practical Approach to Using Statistics in Health Research: From Planning to Reporting. Wiley, Hoboken (2018)

    Book  Google Scholar 

  51. Feng, Y.C., Huang, Y.C., Ma, X.M.: The application of student’s t-test in internal quality control of clinical laboratory. Front. Lab. Med. 1(3), 125–128 (2017)

    Article  Google Scholar 

  52. Wang, D., Zhang, H., Liu, R., Lv, W., Wang, D.: t-test feature selection approach based on term frequency for text categorization. Pattern Recogn. Lett. 45, 1–10 (2014)

    Article  Google Scholar 

  53. Peng, L., Tong, T.: A note on a two-sample T test with one variance unknown. Stat. Method. 8(6), 528–534 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  54. MathWorks, T.: MATLAB (R2019b). The MathWorks, Inc., Natick (2019)

    Google Scholar 

  55. IBM SPSS Statistics for Windows, Version 23.0. IBM Corp., Armonk, NY, USA (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Massachusetts Medical School, Worcester, MA, 01655, USA

    Yeliz Karaca

  2. Galatasaray University, Istanbul, Turkey

    Elgiz Yılmaz Altuntaş

Authors
  1. Yeliz Karaca
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  2. Elgiz Yılmaz Altuntaş
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Correspondence to Yeliz Karaca .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Karaca, Y., Altuntaş, E.Y. (2020). Decision Tree-Based Transdisciplinary Systems Modelling for Cognitive Status in Neurological Diseases. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12250. Springer, Cham. https://doi.org/10.1007/978-3-030-58802-1_32

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  • DOI: https://doi.org/10.1007/978-3-030-58802-1_32

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-58802-1

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