Using Data Mining Algorithm for Assigning Family-Centered Empowerment Model as to Improve the Quality of Life in Cardiac Infarction Patients

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.