Using Data Mining Algorithm for Assigning Family-Centered Empowerment Model as to Improve the Quality of Life in Cardiac Infarction Patients
Archives of Advances in Biosciences,
Vol. 11 No. 3 (2020),
26 September 2020
,
Page 1-12
https://doi.org/10.22037/aab.v11i3.31669
Abstract
Introduction: Today, cardiovascular disease is a major threat to advanced human societies, and is acting as a major cause of disability in many aspects of a patient and family members' lives, including their quality of life. Therefore, the aim of the present study is to provide models for classifying and determining the factors influencing the allocation of family-centered empowerment model to further improve the psychological quality of life of these patients.
Materials and Methods: In this study, data from a clinical trial study were used in which 70 patients with myocardial infarction who randomly received a family-centered empowerment pattern and control group. A model of linear mixed effects and then learning algorithms were used to predict the success or failure of the empowerment model.
Results: In this study, the decision tree model was able to accurately predict more than 96% of patients (Kappa=0.828, ROC=0.96). Physical functions, walking status, creatinine level, EF level, employment status, gender, stress level and body mass index were identified as the effective factors in assigning a family-centered empowerment pattern (P value <0.05). This process was done through software of SPSS24, SAS9.1 and WEKA 3.6.9
Conclusion: The decision tree model was able to correctly classify more than 96% of patients; if a family-centered empowerment model was assigned, this model would improve the psychological dimension of their quality of life.
- Family-Centered Empowerment Model, Quality of Life, Cardiac Infarction, Data Mining, Longitudinal Study
How to Cite
References
Rahimi, A., Concept of spirituality: a conventional content analysis. Journal of Qualitative Research in Health Sciences. 2013; 2(1): p. 11-20.
Otterstad, J.E., Influence on lifestyle measures and five-year coronary risk by a comprehensive lifestyle intervention programme in patients with coronary heart disease. European Journal of Cardiovascular Prevention & Rehabilitation. 2003; 10(6): p. 429-437.
Sebern, M. and B. Riegel, Contributions of supportive relationships to heart failure self-care. European Journal of Cardiovascular Nursing. 2009; 8(2): p. 97-104.
Vahedian azimi A, A.F., Educational challenges in ethical decision making in nursing. Iranian
Journal of Medical Ethics and History of Medicine. 2008.
Heidari, S., A. Salahshoorian, and F. Rafiee, Relationship between perceived social support and social network with quality of life in cancer patients. Journal of Feyz. 2008; 12(2): p. 101-8.
Johansson P, B.A., Dahlsron U, Alehagen U., Global perceived health and health-related quality of life in elderly primary care patients with symptoms of heart failure. Europ J Cardiov Nurs, 2008.
Fazekas, C., B. Semlitsch, and W. Pieringer, Empowerment in the Case of Diabetes mellitus: Theory and Practice. Wiener Medizinische Wochenschrift. 2003; 153(21-22): p. 459-463.
Funnell, M.M. and R.M. Anderson, Empowerment and self-management of diabetes. Clinical diabetes. 2004; 22(3): p. 123-127.
Alhani F, N.S., Kimiagar S, Kazemnejad A, Hidarnia A., Designing family-centered
empowerment model and measuting the effect on the prevention of iron deficinecy in adolesecnt girls.
Pajohandeh.2003.
Andrews, N. and H. Cho, Validating effectiveness of subgroup identification for longitudinal data. Statistics in medicine. 2018; 37(1): p. 98-106.
Fitzmaurice, G.M., N.M. Laird, and J.H. Ware, Applied longitudinal analysis. Vol. 998. 2012: John Wiley & Sons.
Chatfield, C., J. Zidek, and J. Lindsey, An introduction to generalized linear models. 2010.
Diggle, P., et al., Analysis of longitudinal data. 2002: Oxford University Press.
Çığşar, B. and D. Ünal, Comparison of data mining classification algorithms determining the default risk. Scientific Programming, 2019.
Norouzi, M., A. Souri, and M. Samad Zamini, A data mining classification approach for behavioral malware detection. Journal of Computer Networks and Communications. 2016.
Zou, Q., et al., Scalable data mining algorithms in computational biology and biomedicine. 2017; Hindawi.
Yeh, Y.-F. and C.-P. Chang, Applying data mining techniques to identify suitable activities. Mathematical Problems in Engineering, 2015.
Vahedian Azimi A, A.F., Ahmadi F, Kazemnejad A, Effect of family-centered empowerment
model on the life style of myocardial infarction patients. Iranian Journal of Critical Care Nursing. 2010.
Vahedian Azimi A, A.F., Hedayat K., Barriers and Facilitators of Patient's Education: Nurses’
Perspectives. Iranian Journal of Medical Education. 2012; 11(6): 620-34.
Vahedian Azimi A, P.B.M., Gohari Moghaddam K, A Survey on nurses clinical
problems in patient education. . Journal of Nursing and Midwifery Urmia University of Medical
Sciences. 2011; 9(4): 11-16.
Liaw, A. and M. Wiener, Classification and regression by randomForest. R news. 2002; 2(3): p. 18-22.
Harper, P.R., A review and comparison of classification algorithms for medical decision making. Health Policy. 2005; 71(3): p. 315-331.
Kiang, M.Y., A comparative assessment of classification methods. Decision support systems. 2003; 35(4): p. 441-454.
Tu, J.V., Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of clinical epidemiology. 1996; 49(11): p. 1225-1231.
Pohar, M., M. Blas, and S. Turk, Comparison of logistic regression and linear discriminant analysis: a simulation study. Metodoloski zvezki. 2004; 1(1): p. 143.
Dreiseitl, S. and L. Ohno-Machado, Logistic regression and artificial neural network classification models: a methodology review. Journal of biomedical informatics. 2002; 35(5-6): p. 352-359.
- Abstract Viewed: 229 times
- PDF Downloaded: 207 times