Tuberculosis Disease Diagnosis Using Artificial Immune Recognition System

Shamshirband, Shahaboddin; Hessam, Somayeh; Javidnia, Hossein; Amiribesheli, Mohsen; Vahdat, Shaghayegh; Petković, Dalibor; Gani, Abdullah; Kiah, Miss Laiha Mat

doi:10.7150/ijms.8249

Cited by 42 publications

(23 citation statements)

References 36 publications

(42 reference statements)

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“…In another study on predicting type 2 diabetes among pregnant women, Lin et al (2011) used AIRS and compared its result with that of SVM and logistic regression; according to their report, AIRS achieved highest classification accuracy among these three classifiers, the obtained accuracy with AIRS was 62.8 % on the diabetes data set. Recently, Shamshirband et al (2014) used AIRS and fuzzy labeling to diagnose tuberculosis disease. They have reported that their method achieved an accuracy of 99.14 %.…”

Section: Application Of Airs In "Diagnosing Diseases"mentioning

confidence: 99%

RETRACTED ARTICLE: Diagnosing breast cancer with an improved artificial immune recognition system

et al. 2015

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Breast cancer is the top cancer in women worldwide. Scientists are looking for early detection strategies which remain the cornerstone of breast cancer control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose this 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. To increase the classification accuracy, this study introduces a new hybrid system that incorporates support vector machine, fuzzy logic, and real tournament selection mechanism into AIRS. The Wisconsin Breast Cancer data set was used as the benchmark data set; it is available through the machine learning repository of the University of California at Irvine. The classification performance was measured through tenfold cross-validation, student's t test, sensitivity and specificity. With an accuracy of 100 %, the proposed method was able to classify breast cancer dataset successfully.

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Section: Application Of Airs In "Diagnosing Diseases"mentioning

confidence: 99%

RETRACTED ARTICLE: Diagnosing breast cancer with an improved artificial immune recognition system

et al. 2015

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“…In another study on predicting type 2 diabetes among pregnant women, Lin et al [33] used AIRS and compared its result with that of SVM and logistic regression; according to their report, AIRS achieved highest classification accuracy among these three classifiers, the obtained accuracy with the AIRS was 62.8 % on the diabetes data set. Recently, Shamshirband et al [47] used AIRS and fuzzy labeling to diagnose TB disease. They have reported that their method achieved an accuracy of 99.14 %.…”

Section: Application Of Airs In "Diagnosing Diseases"mentioning

confidence: 99%

RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system

Saybani

Shamshirband

Golzari

et al. 2015

Med Biol Eng Comput

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Tuberculosis is a major global health problem that has been ranked as the second leading cause of death from an infectious disease worldwide, after the human immunodeficiency virus. Diagnosis based on cultured specimens is the reference standard; however, results take weeks to obtain. Slow and insensitive diagnostic methods hampered the global control of tuberculosis, and scientists are looking for early detection strategies, which remain the foundation of tuberculosis control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose the disease. The objective of this study is to diagnose tuberculosis using a machine learning method. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy, this study introduces a new hybrid system that incorporates real tournament selection mechanism into the AIRS. This mechanism is used to control the population size of the model and to overcome the existing selection pressure. Patient epacris reports obtained from the Pasteur laboratory in northern Iran were used as the benchmark data set. The sample consisted of 175 records, from which 114 (65 %) were positive for TB, and the remaining 61 (35 %) were negative. The classification performance was measured through tenfold cross-validation, root-mean-square error, sensitivity, and specificity. With an accuracy of 100 %, RMSE of 0, sensitivity of 100 %, and specificity of 100 %, the proposed method was able to successfully classify tuberculosis cases. In addition, the proposed method is comparable with top classifiers used in this research.

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“…This technique implemented the cooperative defense counter‐attack scenarios for the sink node and the base station to operate as rational decision‐maker players through a game theory strategy. Tuberculosis (TB) disease diagnosis system is presented in …”

Section: Motivationmentioning

confidence: 99%

OW‐SVM: Ontology and whale optimization‐based support vector machine for privacy‐preserved medical data classification in cloud

Karlekar

Gomathi

2018

Int J Communication

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Summary Cloud is a multitenant architecture that allows the cloud users to share the resources via servers and is used in various applications, including data classification. Data classification is a widely used data mining technique for big data analysis. It helps the learners to discover hidden data patterns by training massive data collected from the real world. Because this trained model is the private asset of an entity, it should be protected from all other noncollaborative entities. Therefore, it is essential to take effective measures to preserve the confidential data. The objective of this paper is to preserve the privacy of the confidential data in the cloud environment by introducing the medical data classification method. In view of that, this paper presents a method for medical data classification using a novel ontology and whale optimization‐based support vector machine (OW‐SVM) approach. Initially, privacy‐preserved data are developed adopting Kronecker product bat approach, and then, ontology is built for the feature selection process. Ontology and whale optimization‐based support vector machine is then proposed by integrating ontology and whale optimization algorithm into SVM, in which ontology and whale optimization algorithm is used for the feasible selection of kernel parameters. The experiment is done using 3 heart disease datasets, such as Cleveland, Switzerland, and Hungarian. In a comparative analysis, the performance of the OW‐SVM approach is compared with that of K‐nearest neighbor, Naive Bayes, decision tree, SVM, and OW‐SVM, using accuracy, sensitivity, specificity, and fitness, as the evaluation metrics. The OW‐SVM approach could achieve maximum performance with accuracy of 83.21%, the sensitivity of 91.49%, specificity of 73%, and fitness of 81.955, outperforming existing comparative techniques.

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Tuberculosis Disease Diagnosis Using Artificial Immune Recognition System

Cited by 42 publications

References 36 publications

RETRACTED ARTICLE: Diagnosing breast cancer with an improved artificial immune recognition system

RETRACTED ARTICLE: Diagnosing breast cancer with an improved artificial immune recognition system

RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system

OW‐SVM: Ontology and whale optimization‐based support vector machine for privacy‐preserved medical data classification in cloud

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