Asthma Control Level Assessment by Moving from the Current Reactive Care Models into a Preventive Approach based on Fuzzy Clustering and Classification Algorithms

Background and Aim: Asthma is a common and chronic disease of respiratory tracts. The best way to treat Asthma is to control it. Experts of this field suggest the continues monitoring on Asthma symptoms and adjustment of self-care plan with offering the preventive treatment program to have desired c...

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Main Authors: Roghaye khasha, Mohammad Mahdi Sepehri, Nasrin Taherkhani
Format: Article
Language:fas
Published: Tehran University of Medical Sciences 2020-07-01
Series:پیاورد سلامت
Subjects:
Online Access:http://payavard.tums.ac.ir/article-1-6994-en.html
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spelling doaj-c39217b55dff46dc9b7fc89dd25d046b2021-10-02T18:57:23ZfasTehran University of Medical Sciencesپیاورد سلامت1735-81322008-26652020-07-01143201214Asthma Control Level Assessment by Moving from the Current Reactive Care Models into a Preventive Approach based on Fuzzy Clustering and Classification AlgorithmsRoghaye khasha0Mohammad Mahdi Sepehri1Nasrin Taherkhani2 Ph.D. in Information Technology Engineering, Center of Excellence in Healthcare Systems Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran Professor, Department of Healthcare Systems Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran Instructor, Department of Computer Engineering, Faculty of Engineering, Payam-e-Noor University, Saveh, Iran Background and Aim: Asthma is a common and chronic disease of respiratory tracts. The best way to treat Asthma is to control it. Experts of this field suggest the continues monitoring on Asthma symptoms and adjustment of self-care plan with offering the preventive treatment program to have desired control over Asthma. Presenting these plans by the physician is set based on the control level in which the patient is. Therefore, successful recognition and classification of the disease control level can play an important role in presenting the treatment program to the patient and improves the self-care and strengthens the early interventions to alleviate the Asthma symptoms.   Materials and Methods: Based on this objective, we collected the data of 96 Asthma patients within a 9-month period from a specialized hospital for pulmonary diseases in Tehran. Then we classified the Asthma control level by fuzzy clustering and different types of data mining method within a multivariate dataset with the multi-class response variable. Results: Our best model resulting from the balancing operations and feature selection on data have yielded the accuracy of 88%. Conclusion: Our proposed model can be applied in electronic Asthma self-care systems to support the decision in real time and personalized warnings on the possible deterioration of Asthma control. Such tools can centralize the Asthma treatment from the current reactive care models into a preventive approach in which the physician’s decisions and therapeutic actions are resulting from the personal patterns of chronic Asthma control and prevention of acute Asthma.http://payavard.tums.ac.ir/article-1-6994-en.htmlasthma controlpreventiveclusteringclassificationself-care
collection DOAJ
language fas
format Article
sources DOAJ
author Roghaye khasha
Mohammad Mahdi Sepehri
Nasrin Taherkhani
spellingShingle Roghaye khasha
Mohammad Mahdi Sepehri
Nasrin Taherkhani
Asthma Control Level Assessment by Moving from the Current Reactive Care Models into a Preventive Approach based on Fuzzy Clustering and Classification Algorithms
پیاورد سلامت
asthma control
preventive
clustering
classification
self-care
author_facet Roghaye khasha
Mohammad Mahdi Sepehri
Nasrin Taherkhani
author_sort Roghaye khasha
title Asthma Control Level Assessment by Moving from the Current Reactive Care Models into a Preventive Approach based on Fuzzy Clustering and Classification Algorithms
title_short Asthma Control Level Assessment by Moving from the Current Reactive Care Models into a Preventive Approach based on Fuzzy Clustering and Classification Algorithms
title_full Asthma Control Level Assessment by Moving from the Current Reactive Care Models into a Preventive Approach based on Fuzzy Clustering and Classification Algorithms
title_fullStr Asthma Control Level Assessment by Moving from the Current Reactive Care Models into a Preventive Approach based on Fuzzy Clustering and Classification Algorithms
title_full_unstemmed Asthma Control Level Assessment by Moving from the Current Reactive Care Models into a Preventive Approach based on Fuzzy Clustering and Classification Algorithms
title_sort asthma control level assessment by moving from the current reactive care models into a preventive approach based on fuzzy clustering and classification algorithms
publisher Tehran University of Medical Sciences
series پیاورد سلامت
issn 1735-8132
2008-2665
publishDate 2020-07-01
description Background and Aim: Asthma is a common and chronic disease of respiratory tracts. The best way to treat Asthma is to control it. Experts of this field suggest the continues monitoring on Asthma symptoms and adjustment of self-care plan with offering the preventive treatment program to have desired control over Asthma. Presenting these plans by the physician is set based on the control level in which the patient is. Therefore, successful recognition and classification of the disease control level can play an important role in presenting the treatment program to the patient and improves the self-care and strengthens the early interventions to alleviate the Asthma symptoms.   Materials and Methods: Based on this objective, we collected the data of 96 Asthma patients within a 9-month period from a specialized hospital for pulmonary diseases in Tehran. Then we classified the Asthma control level by fuzzy clustering and different types of data mining method within a multivariate dataset with the multi-class response variable. Results: Our best model resulting from the balancing operations and feature selection on data have yielded the accuracy of 88%. Conclusion: Our proposed model can be applied in electronic Asthma self-care systems to support the decision in real time and personalized warnings on the possible deterioration of Asthma control. Such tools can centralize the Asthma treatment from the current reactive care models into a preventive approach in which the physician’s decisions and therapeutic actions are resulting from the personal patterns of chronic Asthma control and prevention of acute Asthma.
topic asthma control
preventive
clustering
classification
self-care
url http://payavard.tums.ac.ir/article-1-6994-en.html
work_keys_str_mv AT roghayekhasha asthmacontrollevelassessmentbymovingfromthecurrentreactivecaremodelsintoapreventiveapproachbasedonfuzzyclusteringandclassificationalgorithms
AT mohammadmahdisepehri asthmacontrollevelassessmentbymovingfromthecurrentreactivecaremodelsintoapreventiveapproachbasedonfuzzyclusteringandclassificationalgorithms
AT nasrintaherkhani asthmacontrollevelassessmentbymovingfromthecurrentreactivecaremodelsintoapreventiveapproachbasedonfuzzyclusteringandclassificationalgorithms
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