Discrete separation of patients’ profiles for chronical obstructive pulmonary disease context-aware healthcare efficient systems

Hamid Mcheick, Farah Diab


According to the Public Health Agency of Canada (PHAC), the symptoms of chronic obstructive pulmonary disease (COPD) are shortness of breath, coughing, and sputum production. Many studies estimate that COPD will become the third-leading cause of death worldwide by 2030 (WHO, 2008). Pervasive healthcare systems cover healthcare issues, including chronic diseases; they help patients to manage their own health information and healthcare services at any time and in any place. We developed a COPD healthcare system based on a combination of the parameters of patients. The main goal is to avoid the severe phases of the disease by monitoring them. This combination of risk factors provides in total 600 profiles from data, with 88.5% accuracy. However, many studies have focused on and shown the issues of the effectiveness and accuracy of these systems. The problem is to apply a new classification model to detect the severe phases of the disease early. Therefore, instead of working on COPD parameters, we design and validate a profile-based classification model of patients. This model will facilitate the building of a rule-based framework. In addition, the accuracy of our extended COPD system is improved using the classification and separation of patients’ profiles.


Classification of profiles; Data combination of chronic obstructive pulmonary disease; Efficiency of context-aware; Healthcare systems; Machine learning; Rule-based system; Separation of concerns

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DOI: http://doi.org/10.11591/ijai.v12.i3.pp1508-1520


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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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