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

Abstract

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 i 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.

A new framework to enhance healthcare monitoring using patient-centric predictive analysis

“…The World Health Organization (WHO) found that IoT can help save lives by facilitating early disease identification and management, as well as increasing access to care and decreasing medical errors. Rapid expansion and increased importance in terms of both income and employment have been characteristics of the healthcare sector in recent years [4]. Just a few short years ago, medical professionals needed to perform a thorough physical examination of a patient to diagnose illness or abnormalities.…”