Medical application of information gain-based artificial immune recognition system (IG-AIRS): Classification of microorganism species

Kara, Sadık; Aksebzeci, Bekir Hakan; Kodaz, Halife; Güneş, Salih; Kaya, Esma; Özbilge, Hatice

doi:10.1016/j.eswa.2008.06.029

Cited by 19 publications

(12 citation statements)

References 20 publications

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“…Another medical application of AIRS can be seen in the work of Kara et al (2009) they used information gain-based AIRS to classify microorganism species, they have achieved a classification accuracy of 92.35 %. A recent application of AIRS for use in the field of medical diagnosis was developed by Chikh et al (2012) who presented a model to classify diabetes diseases.…”

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RETRACTED ARTICLE: Diagnosing breast cancer with an improved artificial immune recognition system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RETRACTED ARTICLE: Diagnosing breast cancer with an improved artificial immune recognition system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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“…Figure 1 shows a flowchart of the principle of this learning process. A brief description of the AIS algorithm that effectively mimics this biological process is shown below [12, 14]:…”

Section: Methods Descriptionmentioning

confidence: 99%

“…Recently, a novel artificial intelligence method termed the artificial immune system (AIS) has been applied to different application areas especially in the pattern recognition field, since it emerged in the 1990s as a bio-inspired computational research tool [11–14]. The main concept of this approach is to use a supervised learning process to create core (representative) data points to represent and cover the sample distribution space of each class.…”

Section: Introductionmentioning

confidence: 99%

A modified artificial immune system based pattern recognition approach—An application to clinical diagnostics

Zhao

Davis

2011

Artificial Intelligence in Medicine

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Objective This paper introduces a modified artificial immune system (AIS)-based pattern recognition method to enhance the recognition ability of the existing conventional AIS-based classification approach and demonstrates the superiority of the proposed new AIS-based method via two case studies of breast cancer diagnosis. Methods and materials Conventionally, the AIS approach is often coupled with the k nearest neighbor (k-NN) algorithm to form a classification method called AIS-kNN. In this paper we discuss the basic principle and possible problems of this conventional approach, and propose a new approach where AIS is integrated with the radial basis function – partial least square regression (AIS-RBFPLS). Additionally, both the two AIS-based approaches are compared with two classical and powerful machine learning methods, back-propagation neural network (BPNN) and orthogonal radial basis function network (Ortho-RBF network). Results The diagnosis results show that: (1) both the AIS-kNN and the AIS-RBFPLS proved to be a good machine leaning method for clinical diagnosis, but the proposed AIS-RBFPLS generated an even lower misclassification ratio, especially in the cases where the conventional AIS-kNN approach generated poor classification results because of possible improper AIS parameters. For example, based upon the AIS memory cells of “replacement threshold = 0.3”, the average misclassification ratios of two approaches for study 1 are 3.36% (AIS-RBFPLS) and 9.07% (AIS-kNN), and the misclassification ratios for study 2 are 19.18% (AIS-RBFPLS) and 28.36% (AIS-kNN); (2) the proposed AIS-RBFPLS presented its robustness in terms of the AIS-created memory cells, showing a smaller standard deviation of the results from the multiple trials than AIS-kNN. For example, using the result from the first set of AIS memory cells as an example, the standard deviations of the misclassification ratios for study 1 are 0.45% (AIS-RBFPLS) and 8.71% (AIS-kNN) and those for study 2 are 0.49% (AIS-RBFPLS) and 6.61% (AIS-kNN); and (3) the proposed AIS-RBFPLS classification approaches also yielded better diagnosis results than two classical neural network approaches of BPNN and Ortho-RBF network. Conclusion In summary, this paper proposed a new machine learning method for complex systems by integrating the AIS system with RBFPLS. This new method demonstrates its satisfactory effect on classification accuracy for clinical diagnosis, and also indicates its wide potential applications to other diagnosis and detection problems.

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“…Another medical application of the AIRS can be seen in the work of Kara et al; they used information gain-based AIRS to classify microorganism species, and they have achieved a classification accuracy of 92.35 % [25]. A recent application of the AIRS in the field of medical diagnosis was developed by Chikh et al [8], and they developed a model to classify diabetes diseases.…”

Section: Application Of Airs In "Diagnosing Diseases"mentioning

confidence: 98%

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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Medical application of information gain-based artificial immune recognition system (IG-AIRS): Classification of microorganism species

Cited by 19 publications

References 20 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

A modified artificial immune system based pattern recognition approach—An application to clinical diagnostics

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

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