@article{10.11648/j.ijiis.20211005.11,
  author = {Konan-Marcelin Kouamé and Hamid Mcheick},
  title = {Designing Adaptive Mechanism for COVID-19 and Exacerbation in Cases of COPD Patients Using Machine Learning Approaches},
  journal = {International Journal of Intelligent Information Systems},
  volume = {10},
  number = {5},
  pages = {81-97},
  doi = {10.11648/j.ijiis.20211005.11},
  url = {https://doi.org/10.11648/j.ijiis.20211005.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20211005.11},
  abstract = {The technology of machine learning has been widely applied in several domains and complex medical problems, specifically in chronic obstructive pulmonary disease (COPD). Researchers in the field of respiratory diseases confirm that people who suffer from COPD have high risks when exposed to COVID-19. The most common oncoming COPD exacerbations and COPD symptoms of COVID-19 are congruent. The distinction between COPD exacerbations and COVID-19 with COPD is nearly impossible without testing. This paper proposes a new powerful model for classifying COPD patients with exacerbations and those with COVID-19 using machine learning and deep learning algorithms. The major contribution of this research is the dynamic classification process based on the patient context that can help detect exacerbations or COVID-19 per period. Indeed, Five Machine Learning algorithms are trained, tested and a performant classification model is identified. This prediction model is then associated with a dynamic COPD patient context for monitoring the patient's health status. This model based on the dynamic adaptation mechanism combined with a classification contributes to identifying dynamically COPD exacerbations and COVID-19 symptoms for COPD patients. Indeed, periodically, data on a new patient is injected into the prediction model. At the output of the model, the patient is either classified in the exacerbation category, or classified in the COVID-19 category, or no category. By period. A dynamic dashboard of classified patients is available to help medical staff take appropriate decisions. This approach helps to follow the evolution of COPD patient comorbidities (exacerbation, COVID-19). Finally, classification would allow healthcare stakeholders to provide healthcare service according to the patient’s status. The methodology of research consists of designing and implementing a dynamic model for classifying COPD patients. Since early intervention is associated with improved prognosis, with our solution, healthcare staff can identify COPD patients who are most at risk of developing exacerbation or COVID-19. Consequently, upon admission, this will ensure that these patients receive appropriate care as soon as possible.},
 year = {2021}
}

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TY  - JOUR
T1  - Designing Adaptive Mechanism for COVID-19 and Exacerbation in Cases of COPD Patients Using Machine Learning Approaches
AU  - Konan-Marcelin Kouamé
AU  - Hamid Mcheick
Y1  - 2021/10/30
PY  - 2021
N1  - https://doi.org/10.11648/j.ijiis.20211005.11
DO  - 10.11648/j.ijiis.20211005.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 81
EP  - 97
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20211005.11
AB  - The technology of machine learning has been widely applied in several domains and complex medical problems, specifically in chronic obstructive pulmonary disease (COPD). Researchers in the field of respiratory diseases confirm that people who suffer from COPD have high risks when exposed to COVID-19. The most common oncoming COPD exacerbations and COPD symptoms of COVID-19 are congruent. The distinction between COPD exacerbations and COVID-19 with COPD is nearly impossible without testing. This paper proposes a new powerful model for classifying COPD patients with exacerbations and those with COVID-19 using machine learning and deep learning algorithms. The major contribution of this research is the dynamic classification process based on the patient context that can help detect exacerbations or COVID-19 per period. Indeed, Five Machine Learning algorithms are trained, tested and a performant classification model is identified. This prediction model is then associated with a dynamic COPD patient context for monitoring the patient's health status. This model based on the dynamic adaptation mechanism combined with a classification contributes to identifying dynamically COPD exacerbations and COVID-19 symptoms for COPD patients. Indeed, periodically, data on a new patient is injected into the prediction model. At the output of the model, the patient is either classified in the exacerbation category, or classified in the COVID-19 category, or no category. By period. A dynamic dashboard of classified patients is available to help medical staff take appropriate decisions. This approach helps to follow the evolution of COPD patient comorbidities (exacerbation, COVID-19). Finally, classification would allow healthcare stakeholders to provide healthcare service according to the patient’s status. The methodology of research consists of designing and implementing a dynamic model for classifying COPD patients. Since early intervention is associated with improved prognosis, with our solution, healthcare staff can identify COPD patients who are most at risk of developing exacerbation or COVID-19. Consequently, upon admission, this will ensure that these patients receive appropriate care as soon as possible.
VL  - 10
IS  - 5
ER  - 

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