Rough set based lattice structure for knowledge representation in medical expert systems: Low back pain management case study

Santra, Debarpita; Basu, Swapan; Mandal, Jyotsna Kumar; Goswami, Subrata

doi:10.1016/j.eswa.2019.113084

Cited by 24 publications

(15 citation statements)

References 55 publications

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“…To deal with complex problems and difficult to define with a mathematical model with an approach reasoning process, this fuzzy system is good enough to be implemented [32]. The reasoning ability of the fuzzy system is almost similar to the ability of human reasoning where the fuzzy system can respond to qualitative, inaccurate and ambiguous information [33]. Some of the reasons for using fuzzy logic are that fuzzy logic is very flexible, has tolerance, is easy to understand, whereas the mathematical concepts underlying fuzzy reasoning are very simple [34].…”

Section: Fuzzy Logic Methodsmentioning

confidence: 99%

“…Fuzzy logic can be interpreted as a precise mapping of an input space into an output space [33]. Fuzzy logic is usually used for solving a complex problem by adding new input functions to the fuzzy system where the system works based on the principles and rules associated with it [34].…”

Section: Expert Systemmentioning

confidence: 99%

“…The function of the inference engine in the expert system is used to trace the reasoning process to a condition experienced based on a knowledge base of available facts and rules [6]. In the development environment, there is expert knowledge about a field that is absorbed by knowledge engineering to be translated into facts and rules which will later be used in the knowledge base and correlated with the inference engine in the expert system [33]. In this study, the expert is a paediatrician and a psychologist who provides knowledge about autism spectrum disorder which consists of symptoms experienced and therapy that can be done by observing patients [3].…”

Section: Expert Systemmentioning

confidence: 99%

See 2 more Smart Citations

Optimization of the Fuzzy Logic Method for Autism Spectrum Disorder Diagnosis

Wanti¹,

Puspitasari

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Diagnosis of autism spectrum disorder (ASD) can use a fuzzy inference system. The use of fuzzy logic method to obtain ASD diagnosis results according to experts based on the limits of factors/symptoms of the disease and all the rules obtained from experts. Recommendations for therapy and preventive actions can be given by experts after knowing the results of the diagnosis of ASD using the fuzzy logic method. This study serves to diagnose ASD by optimizing each degree of membership in the fuzzy logic method with the Mamdani method approach which is involved in the autism detection process involving 96 patient data. The Mamdani method itself can process an uncertain value from the user/patient into a definite value whose membership degree can be determined and adjusted to the conditions of the problem. Optimization was carried out on the degree of membership for all variables involved in the process of diagnosing ASD, namely social interaction, social communication and imagination and behavior patterns. The results of this study indicate a relatively small level of fuzzy calculation error with a precision value of 94.4%, a recall precision value of 65.4% and an error rate value of 3.05%. Calculation of accuracy shows a result of 90.59%.

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Section: Fuzzy Logic Methodsmentioning

confidence: 99%

Section: Expert Systemmentioning

confidence: 99%

Section: Expert Systemmentioning

confidence: 99%

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Optimization of the Fuzzy Logic Method for Autism Spectrum Disorder Diagnosis

Wanti¹,

Puspitasari

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…As in the new word recognition method proposed in [10,20], the left adjacent entropy and the right adjacent entropy of a word string are calculated. When the left and right adjacent entropies are greater than a threshold, the word string is considered to be an independent language unit, and the word string is considered [21][22][23]. This paper referenced the above ideas into Uyghur language research and found that it was suitable for the needs of texts to judge the problem by 'connecting' and 'breaking' the words, based on the entropy.…”

Section: Entropy Of Adjacencymentioning

confidence: 99%

Automatic extraction of Uyghur domain concepts based on multi‐feature for ontology extension

et al. 2020

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In the internet age, as a conceptual model of knowledge organisation, ontology has become a research hotspot. Ontology extension achieves the purpose of expanding ontology by adding new concepts and discovering the relationships between concepts in the existing ontology. In order to improve the automation and accuracy of ontology concept extraction in the Uyghur language, here the authors propose a new method to automatically extract concepts from text collection. For the characteristics of the Uyghur domain ontology concept, the text preprocessing is performed first, then the inter-word correlation of multi-feature fusion is calculated, such as Mi, Cd and Ea, and finally, the domain terminology and concept are automatically extracted, according to the term frequency-inverse document frequency algorithm. Experiment results show that, in terms of precision and recall rate, the multi-feature method proposed here represents an improvement over other methods. It also proves the feasibility and effectiveness of the authors' method to extract the domain concept.

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“…Pawlak proposed the rough set theory [1], which rapidly attracted the attention of scholars and was successfully applied to machine learning [2,3], formal concept analysis [4][5][6], knowledge engineering [7,8], granular computing [9,10], etc. The rough set mainly utilizes the equivalence relation to express the classification ability of domain knowledge.…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy Three-Way Decision Model Based on the Three-Way Granular Computing Method

2020

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Three-way decisions, as a general model for uncertain information processing and decisions, mainly utilize the threshold generated by the decision cost matrix to determine the decision category of the object. However, the determination of the threshold is usually accompanied by varying degrees of subjectivity. In addition, the potential symmetrical relationship between the advantages and disadvantages of the decision cost is also a problem worthy of attention. In this study, we propose a novel intuitionistic fuzzy three-way decision (IFTWD) model based on a three-way granular computing method. First, we present the calculation methods for the possibility of membership state and non-membership state, as well as prove the related properties. Furthermore, we investigate the object information granules, i.e., the fine-grained, medium-grained, and coarse-grained objects, by combining the state probability distribution and probability distribution. Then, for decision and evaluation issues, we define the superiority-compatibility relation and inferiority-compatibility relation for IFTWD model construction. In addition, we use the superiority degree and inferiority degree instead of the original thresholds and design a new method for evaluating decision cost. Finally, we focus on the algorithm research of the proposed model and present an empirical study of agricultural ecological investment in Hubei Province to demonstrate the effectiveness of our model.

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Rough set based lattice structure for knowledge representation in medical expert systems: Low back pain management case study

Cited by 24 publications

References 55 publications

Optimization of the Fuzzy Logic Method for Autism Spectrum Disorder Diagnosis

Optimization of the Fuzzy Logic Method for Autism Spectrum Disorder Diagnosis

Automatic extraction of Uyghur domain concepts based on multi‐feature for ontology extension

Intuitionistic Fuzzy Three-Way Decision Model Based on the Three-Way Granular Computing Method

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