Issues and Challenges of Intelligent Systems and Computational Intelligence

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Then the whole text was analyzed and investigated and finally 30 articles were selected based on the inclusion criteria. In this review study, articles having the following inclusion criteria have been selected: a the intelligent system described in the article should being the domain of MS diagnosis and classifying its clinical types, b The reasoning method of the system for the diagnosis of MS should be completely described, c Those articles in which picture processing methods and DNA frequency processing have not been used, and d The intelligent computer system of disease diagnosis has been evaluated and its results have been reported numerically or descriptively.

Data extraction has been done based on research questions of the selected papers. The calculation formulas for some of the evaluation parameters are as follows:. Total amount of articles were retrieved by using the applied search strategy in electronic databases Figure 1 , their titles and abstracts were analyzed, and articles were selected. Their texts were completely analyzed, among which 30 articles with high quality were selected based on the inclusion criteria, with a complete description and explanation of the method design, software development and evaluation.

According to Table 1 , different decision making methods have been used in reasoning motors of intelligent systems for MS diagnosis. These methods were divided in to two overall groups of knowledge-based and non-knowledge based. In some articles, integrated methods have been applied, such as integration of rule-based and case-based methods.

Each reasoning method included different models and techniques. According to Table 1 , in 5 articles, the rule-based method with backward chaining techniques 19 - 21 , 23 , 24 , forward chaining techniques 31 , and RETE 22 and ambulation based algorithms 32 were used. According to Table 2 , in article 32 , the Kappa conformity rate between clinical decision support system diagnosis and physician diagnosis was equal to 0.

In case-based reasoning, for solving new problems, previous solutions are adapted to solve similar problems, which is similar to the human intelligence process that uses his experiences for new problems. In articles 34 , 35 , the model-based method was used. The Kappa parameter in this method is equal to 0. Fuzzy logic allows the software variables of the decision support system to be members of different sets, simultaneously and with different degrees.

In 5 articles, the fuzzy logic method with Mamdani 37 - 39 and Sogno 40 fuzzy models and the fuzzy clustering algorithm 31 , 41 were used. Natural language processing is a tool for identifying vocabulary and their mapping to concepts. This method has been applied in three intelligent systems, for which the applicable algorithm included: Definitive type1, Definitive type 2, possible type 1, possible type 2 42 and PERL 43 , Table 2 showed that the efficiency scales of sensitivity and specificity and positive prediction value of these algorithms are very high.

The neural network method is obtained from biological characteristics of the human brain and the individuals reasoning method. According to Table 2 , the RBF neural network in article 46 had the highest accuracy. The SVM method was applied alone in two articles and in combination with other methods in two other articles.

This method is easy, but its average sensitivity and accuracy in articles 50 , 51 was 0.

Solving Health Care Challenges with Machine Learning and Artificial Intelligence

In 6 articles, a combination of different reasoning methods have been used that included a combination of rule-based and case-based methods 23 , 24 , a combination of rule-based and fuzzy logic methods 31 , a combination of artificial neural network, statistical analysis and decision tree methods 45 and a combination of SVM, statistical analysis and neural network methods in studies 48 , According to Table 2 , the efficiency of combining reasoning methods was very high. By applying different reasoning methods, intelligent computer systems help to diagnose MS more accurately.

The rule-based method is one of the best applications of disease diagnosis. This method manages well-defined problems with knowledge-based texts; however, it has limited flexibility. To overcome such a weakness, case-based methods are used. The case-based method is more suitable for domains in which rules and relations among parameters are unknown. The diagnosis of rare and complicated disorders like MS was one of these domains. Natural language expressions are ambiguous in the medical domain and are used abundantly.

The existing uncertainty in this domain has resulted in complexity for the production of medical intelligent systems. Certainty factors and the method of confronting uncertainty are very important characteristics of the fuzzy logic method that have made it unique 56 , Extracting knowledge and specifying fuzzy rules were difficult and tiresome, requiring a great amount of experience and skills. Learning algorithms in the neural network method are effective tools for calculating and data mining in education data and their generalization.

The neural network method was suitable for diagnosing MS because the diagnosis of this disease was done based on many decision parameters and these parameters were various in different patients. The natural language processing method analyzes medical records and identifies MS patients in the primary steps of the clinical course of disease using simple algorithms. Moreover, this method increases diagnosis accuracy and the positive prediction value. However, classification models used well-defined signs and symptoms and did not benefit from terms existing in the clinical notes.

The genetic algorithm in a big and very complicated problem space is seeking for a correct and fast solution, while minimizing the problem space. This approach is applied repeatedly to extract characteristics and classifications. This algorithm requires much calculation and a large memory and may not find the most optimal answer. Some intelligent systems used a combination of reasoning methods. Rule-based systems along with case-based systems completely simulate the decision-making process of the physician. Objective knowledge is in the form of rules and subjective knowledge includes a combination of cases.

The combination of these two methods improved problem solving ability and diagnosis accuracy, simplified the extraction of knowledge, and increased the cost-effectiveness of the system. Articles that applied the rule-based method have suggested that fuzzy rules and weighing out rules be used to solve the uncertainty problem, the combination that was used in the first article.

A combination of neural networks, fuzzy logic and genetic algorithm help to minimize problem space complexity such as MS diagnosis with different signs and symptoms. According to the results of this review, in intelligent systems of MS diagnosis, the rule-based method had the highest application. In the rule-based method, the backward chaining technique, in the neural network method, the multilayer Perceptron network, and in the fuzzy logic method, the Mamdani fuzzy model was used for diagnosis. Table 2 shows that the model-based and evidence-based methods had the highest amount of efficiency among the knowledge-based methods.

The Kappa variable for them was equal to one. According to interpretation of Kappa, Landis and Koch table 36 , knowledge-based methods had good efficiency and the decision support system for MS diagnosis made decisions similar to an expert physician. In the fuzzy logic method, due to various efficiency evaluation scales, the Mamdania and Sogno models and fuzzy clustering algorithm efficiency cannot be compared in MS diagnosis. Results showed that due to similarities in the nature of fuzzy logic and the medical domain, fuzzy systems were useful for MS diagnosis.

The SVM method was less efficient compared to other methods because if the number of characteristics and signs and symptoms of a disease such as MS are high, they do not map the multi dimension space adequately enough. However, due to the simplicity of this method, it has been used alone and in combination with other methods. The number of selected articles in this review article was limited due to lack of availability of the full texts.

In addition, the calculated efficiency scales were different in various articles. Therefore, the efficiencies of some applied methods and algorithms were not comparable with each other. Values recorded in Table 2 showed that the neural network method was more efficient than other reasoning methods in diagnosing MS. Generally, it can be concluded that today, reasoning methods have been highly efficient in diagnosing and predicting clinical MS and can be used in assisting the clinical decision support system to help physicians and patients in the correct and timely diagnosis of this disease.

It is suggested that these reasoning methods can be applied to intelligent systems to diagnose other brain disorders and to compare the results with each other. Nowadays, image processing methods, genetic data processing and analytical methods of the electroencephalogram have been applied in the diagnosis and prediction of this disease, and their efficiency results can be compared and generalized with the results of this review study.

The aim of this study was to provide a general viewpoint on the developments in different methodologies in the intelligent system reasoning of MS diagnosis. The rule-based method was more applicable than other reasoning methods due to its modularity and rule integrity in the knowledge-based system. Then, the fuzzy logic method was applied more due to its unique capability to solve uncertain problems and simplify the knowledge presentation process and minimize the calculation complexity for complex diagnosis of MS.

In addition, the neural network methods were applied to diagnose MS due to their powerful calculation capability in a complex problem space. All reasoning methods had a high efficiency in diagnosing MS, but the efficiency of these methods was different with regard to their characteristics.

Forthcoming articles

The neural network method had higher efficiency than other reasoning methods in MS diagnosis. The fuzzy logic method performed successfully in disease diagnosis due to its medical nature, particularly in neurological disorders. The Kappa scale value showed that there was complete conformity between the intelligent diagnosis system and expert neurologist diagnosis.

The SVM method was less efficient for MS diagnosis than other methods because of its inaccurate mapping in large and complex problem space. Surely, all reasoning methods had some limitations. In the rule-based reasoning method, there was inflexibility and some problems in acquiring knowledge, and updating and maintaining it.

The neural network method works like a black box and parameter specification requires a lot of experience for better performance. In the fuzzy logic method, knowledge extraction, set specification and fuzzy membership functions and fuzzy rules definition were difficult and exhausting.

In order to overcome these limitations, a combination of these methods was used to improve the system ability in accurately diagnosing MS. In general, we can conclude that computer methods and techniques have the potential to be applied in clinical practices and research in MS and other neurological disorders. H,, F. A, and L. Farkhondeh Asadi revised the article critically and also gave final approval of the version to be published in collaboration with Azamossadat Hosseini. National Center for Biotechnology Information , U. Journal List Acta Inform Med v. Acta Inform Med. Iran Find articles by Leila Akramian Arani.

Bibliographic Information

Iran Find articles by Azamossadat Hosseini. Iran Find articles by Farkhondeh Asadi. Seyed Ali Masoud 2 Neurology Department. Iran Find articles by Eslam Nazemi. My PhD, titled "Optimization heuristics for solving technician and task scheduling problems", focused on solving NP-hard combinatorial optimization problems that arise in the real world and was sponsored by industry. The project enabled me to enhance my soft skills, write academically, learn to code and develop a deeper understanding of real-world business problems and innovative ways to solve them. Amy's research area is combinatorial optimisation solving NP-hard scheduling problems.

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