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An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning

  • Roghaye Khasha
  • Mohammad Mehdi SepehriEmail author
  • Seyed Alireza Mahdaviani
Systems-Level Quality Improvement
  • 60 Downloads
Part of the following topical collections:
  1. Systems-Level Quality Improvement

Abstract

Approximately 300 million people are afflicted with asthma around the world, with the estimated death rate of 250,000 cases, indicating the significance of this disease. If not treated, it can turn into a serious public health problem. The best method to treat asthma is to control it. Physicians recommend continuous monitoring on asthma symptoms and offering treatment preventive plans based on the patient’s control level. Therefore, successful detection of the disease control level plays a critical role in presenting treatment plans. In view of this objective, we collected the data of 96 asthma patients within a 9-month period from a specialized hospital for pulmonary diseases in Tehran. A new ensemble learning algorithm with combining physicians’ knowledge in the form of a rule-based classifier and supervised learning algorithms is proposed to detect asthma control level in a multivariate dataset with multiclass response variable. The model outcome resulting from the balancing operations and feature selection on data yielded the accuracy of 91.66%. Our proposed model combines medical knowledge with machine learning algorithms to classify asthma control level more accurately. This model can be applied in electronic self-care systems to support the real-time decision and personalized warnings on possible deterioration of asthma control level. Such tools can centralize asthma treatment from the current reactive care models into a preventive approach in which the physician’s therapeutic actions would be based on control level.

Keywords

Asthma control Ensemble learning Medical knowledge Rule-based Self-care 

Notes

Compliance with ethical standards

Conflicts of interest

None.

Ethical approval

None.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Roghaye Khasha
    • 1
  • Mohammad Mehdi Sepehri
    • 2
    Email author
  • Seyed Alireza Mahdaviani
    • 3
  1. 1.Group of Information Technology, Faculty of Industrial and Systems EngineeringTarbiat Modares UniversityTehranIran
  2. 2.Faculty of Industrial and Systems EngineeringTarbiat Modares UniversityTehranIran
  3. 3.Pediatric Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD)Shahid Beheshti University of Medical SciencesTehranIran

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