Evidence map›Paper›PMID 26023340›Full record

ArticleIranian Red Crescent medical journal2015

Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system.

Mahmoud Reza Saybani, Shahaboddin Shamshirband, Shahram Golzari Hormozi, Teh Ying Wah, Saeed Aghabozorgi, Mohamad Amin Pourhoseingholi, Teodora Olariu

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In one paragraph

Article in Iranian Red Crescent medical journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.3field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 41 citations in OpenAlex.

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  9. Accurate and rapid screening model for potential diabetes mellitus.BMC medical informatics and decision making · 2019
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 3 countries.

Mahmoud Reza SaybaniDepartment of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kula Lumpur, Malaysia.
Shahaboddin ShamshirbandDepartment of Computer Science, Chalous Branch, Islamic Azad University, Chalous, IR Iran.
Shahram Golzari HormoziDepartment of Electrical and Computer Engineering, Faculty of Engineering, University of Hormozgan, Bandar Abbas, IR Iran.
Teh Ying WahDepartment of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kula Lumpur, Malaysia.
Saeed AghabozorgiDepartment of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kula Lumpur, Malaysia.
Mohamad Amin PourhoseingholiGastroenterology and Liver Diseases Research Center, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran.
Teodora OlariuDepartment of Intensive Care, Faculty of Medicine, Western University of Arad, Arad, Romania.
University of Malaya · MYHormozgan University · IRIslamic Azad University of Chalous · IRShahid Beheshti University of Medical Sciences · IRVasile Goldis Western University of Arad · RO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTuberculosis (TB) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnosis based on cultured specimens is the reference standard, however results take weeks to process. Scientists are looking for early detection strategies, which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and quickly diagnose the disease. Artificial Immune Recognition System (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy.

objectivesIn order to increase the classification accuracy of AIRS, this study introduces a new hybrid system that incorporates a support vector machine into AIRS for diagnosing tuberculosis. PATIENTS AND

methodsPatient epacris reports obtained from the Pasteur laboratory of Iran were used as the benchmark data set, with the sample size of 175 (114 positive samples for TB and 60 samples in the negative group). The strategy of this study was to ensure representativeness, thus it was important to have an adequate number of instances for both TB and non-TB cases. The classification performance was measured through 10-fold cross-validation, Root Mean Squared Error (RMSE), sensitivity and specificity, Youden's Index, and Area Under the Curve (AUC). Statistical analysis was done using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning program for windows.

resultsWith an accuracy of 100%, sensitivity of 100%, specificity of 100%, Youden's Index of 1, Area Under the Curve of 1, and RMSE of 0, the proposed method was able to successfully classify tuberculosis patients.

conclusionsThere have been many researches that aimed at diagnosing tuberculosis faster and more accurately. Our results described a model for diagnosing tuberculosis with 100% sensitivity and 100% specificity. This model can be used as an additional tool for experts in medicine to diagnose TBC more accurately and quickly.

Indexed as

Artificial IntelligenceClassificationData MiningExpert SystemsSupport Vector Machines

Identifiers

PMID26023340
PMCPMC4443397
OpenAlexW2151795182

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.