Proposing a two-step Decision Support System (TPIS) based on Stacked
ensemble classifier for early and low cost (step-1) and final (step-2)
differential diagnosis of Mycobacterium Tuberculosis from non-tuberculosis
Pneumonia
- URL: http://arxiv.org/abs/2009.02316v1
- Date: Fri, 4 Sep 2020 17:47:41 GMT
- Title: Proposing a two-step Decision Support System (TPIS) based on Stacked
ensemble classifier for early and low cost (step-1) and final (step-2)
differential diagnosis of Mycobacterium Tuberculosis from non-tuberculosis
Pneumonia
- Authors: Toktam Khatibi, Ali Farahani, Hossein Sarmadian
- Abstract summary: Mycobacterium Tuberculosis (TB) is an infectious bacterial disease presenting similar symptoms to pneumonia.
In this study, a two-step decision support system named TPIS is proposed for differential diagnosis of TB from pneumonia.
- Score: 3.5128547933798275
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Background: Mycobacterium Tuberculosis (TB) is an infectious bacterial
disease presenting similar symptoms to pneumonia; therefore, differentiating
between TB and pneumonia is challenging. Therefore, the main aim of this study
is proposing an automatic method for differential diagnosis of TB from
Pneumonia. Methods: In this study, a two-step decision support system named
TPIS is proposed for differential diagnosis of TB from pneumonia based on
stacked ensemble classifiers. The first step of our proposed model aims at
early diagnosis based on low-cost features including demographic
characteristics and patient symptoms (including 18 features). TPIS second step
makes the final decision based on the meta features extracted in the first
step, the laboratory tests and chest radiography reports. This retrospective
study considers 199 patient medical records for patients suffering from TB or
pneumonia, which has been registered in a hospital in Arak, Iran. Results:
Experimental results show that TPIS outperforms the compared machine learning
methods for early differential diagnosis of pulmonary tuberculosis from
pneumonia with AUC of 90.26 and accuracy of 91.37 and final decision making
with AUC of 92.81 and accuracy of 93.89. Conclusions: The main advantage of
early diagnosis is beginning the treatment procedure for confidently diagnosed
patients as soon as possible and preventing latency in treatment. Therefore,
early diagnosis reduces the maturation of late treatment of both diseases.
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