Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease

Kodaz, Halife; Özşen, Seral; Arslan, Ahmet; Güneş, Salih

doi:10.1016/j.eswa.2008.01.026

Cited by 59 publications

(26 citation statements)

References 16 publications

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“…For example, the current LIS make very limited use of artificial intelligence approaches such as neural 1 or Bayesian 2 networks, fuzzy logic, 3 genetic algorithms, 4 and artificial immune recognition systems. 5 Health care systems in general can be characterized as conservative and resistant to change and current health care information systems (HIS) and LIS are a reflection of this conservative approach. Despite the potential benefits in cost-efficiency and patient care improvements possible with well-designed HIS/LIS, most of these systems lag significantly behind the possibilities afforded by current information technology.…”

mentioning

confidence: 99%

The Ideal Laboratory Information System

Sepulveda

Young

2013

Archives of Pathology &Amp; Laboratory Medicine

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Context.-Laboratory information systems (LIS) are critical components of the operation of clinical laboratories. However, the functionalities of LIS have lagged significantly behind the capacities of current hardware and software technologies, while the complexity of the information produced by clinical laboratories has been increasing over time and will soon undergo rapid expansion with the use of new, high-throughput and high-dimensionality laboratory tests. In the broadest sense, LIS are essential to manage the flow of information between health care providers, patients, and laboratories and should be designed to optimize not only laboratory operations but also personalized clinical care.Objective.-To list suggestions for designing LIS with the goal of optimizing the operation of clinical laboratories while improving clinical care by intelligent management of laboratory information.Data Sources.-Literature review, interviews with laboratory users, and personal experience and opinion.Conclusions.-Laboratory information systems can improve laboratory operations and improve patient care. S ince the 1970s, laboratory information systems (LIS) have been critical components of the operation of clinical laboratories. They were initially developed to collect, record, present, organize, and archive laboratory results, often with a focus on generating information for proper financial management of the laboratory. While information technology in general is advancing at an increasingly faster rate, particularly in the hardware domain but also in software development, LIS have not evolved correspondingly. For example, the current LIS make very limited use of artificial intelligence approaches such as neural 1 or Bayesian 2 networks, fuzzy logic, 3 genetic algorithms, 4 and artificial immune recognition systems.5 Health care systems in general can be characterized as conservative and resistant to change and current health care information systems (HIS) and LIS are a reflection of this conservative approach. Despite the potential benefits in cost-efficiency and patient care improvements possible with well-designed HIS/LIS, most of these systems lag significantly behind the possibilities afforded by current information technology. In fact, some of these systems have deficiencies that most homeand Web-based software have long overcome, such as efficient navigation, rapid response, and spelling correction.Modern clinical laboratories are purveyors of information, in the form of laboratory results, which may be numbers, text, graphs, or other images, together with interpretative data, to assist health care providers in delivering optimal patient care. The complexity of the information produced by clinical laboratories has been increasing over time, and with the advent of large-scale analytic techniques, such as microarrays and next-generation sequencing, the amount of data produced will rapidly grow by several orders of magnitude. Advanced developments in data management and bioinformatics will need to be incorporated into LIS ...

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mentioning

confidence: 99%

The Ideal Laboratory Information System

Sepulveda

Young

2013

Archives of Pathology &Amp; Laboratory Medicine

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“…decision trees [5], k-nearest neighbor classifiers [9], fuzzy classifiers [16,17]; hybrid case-based reasoning [18]; mixture of expert models [10]; immune algorithms [11], immune recognition systems [12]; neuro-fuzzy expert systems [13], and differential evolution [19].…”

Section: Related Researchmentioning

confidence: 99%

“…However, many current diagnostic methods [1][2][3][4][5][6][7][8][9][10][11][12][13]15,18,19] for thyroid disease are black-box models. A drawback of black-box models is that they cannot adequately reveal information that may be hidden in the data.…”

Section: Introductionmentioning

confidence: 99%

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Use of the recursive-rule extraction algorithm with continuous attributes to improve diagnostic accuracy in thyroid disease

Hayashi

Nakano

Fujisawa

2015

Informatics in Medicine Unlocked

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a b s t r a c tThyroid diseases, which often lead to thyroid dysfunction involving either hypo-or hyperthyroidism, affect hundreds of millions of people worldwide, many of whom remain undiagnosed; however, diagnosis is difficult because symptoms are similar to those seen in a number of other conditions. The objective of this study was to assess the effectiveness of the Recursive-Rule Extraction (Re-RX) algorithm with continuous attributes (Continuous Re-RX) in extracting highly accurate, concise, and interpretable classification rules for the diagnosis of thyroid disease. We used the 7200-sample Thyroid dataset from the University of California Irvine Machine Learning Repository, a large and highly imbalanced dataset that comprises both discrete and continuous attributes. We trained the dataset using Continuous Re-RX, and after obtaining the maximum training and test accuracies, the number of extracted rules, and the average number of antecedents, we compared the results with those of other extraction methods. Our results suggested that Continuous Re-RX not only achieved the highest accuracy for diagnosing thyroid disease compared with the other methods, but also provided simple, concise, and interpretable rules. Based on these results, we believe that the use of Continuous Re-RX in machine learning may assist healthcare professionals in the diagnosis of thyroid disease.

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“…A new Artificial immune system named the Information Gain based Artificial Immune Recognition System (IG-AIRS) is proposed by Kodaz et al [37]. The proposed system involves two stages.…”

Section: Artificial Immune Systemmentioning

confidence: 99%