A New Strategy for Case-Based Reasoning Retrieval Using Classification Based on Association

Aljuboori, Ahmed; Meziane, Farid; Parsons, David

doi:10.1007/978-3-319-41920-6_24

Cited by 6 publications

(5 citation statements)

References 37 publications

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“…The Likert scale of each checklist was recoded dichotomously, with 1 indicating the presence of signs of ID, when the scale responses were 'sometimes', 'often', or 'very often', and with 0 when the responses were 'never' or 'rarely'. To generate the predictive models that checked the sensitivity of the checklist items compared to the diagnosis, the Classification Based on Associations (CBA) algorithm (Aljuboori et al, 2016) was used. To produce the models, algorithms were established to identify the checklist items that could be classified as predictors of ID diagnosis.…”

Section: Discussionmentioning

confidence: 99%

“…The CBA Algorithm (classification based on rules) used in this research is based on the a priori algorithm. The difference is that in the data set the class attributed is assigned, and the analysis of the rules is done assuming Y only the diagnosis characteristic (Chen et al, 2006;Thabtah and Cowling, 2007;Aljuboori et al, 2016). The purpose of the CBA, in the context of this research, is to generate rules from the associations checklist items that make it possible to establish the predictive model by checking the sensitivity indicators of the items (from the rules) compared to the diagnosis (Han et al, 2011).…”

Section: Strategic Levelmentioning

confidence: 99%

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DIagnosys: An Analytical Framework for the Identification of Elementary School Students with Intellectual Disability

et al. 2021

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The identification of mild Intellectual Disability (ID) usually occurs late when the demands intrinsic to literacy reveal the typical signs to the educators. The study had two phases. The first phase aimed at developing a computation system (framework), named DIagnosys, an instrument designed to help educators identify students with characteristics compatible with ID, and to describe the operational, tactical, and strategic levels. The second phase verified the framework predictive sensitivity, using an artificial intelligence algorithm. For that purpose, the framework was applied in 51 teachers and their 1,758 students of 2nd and 4th grade, and their respective parents. We collected data using a checklist of signs compatible with ID, the Brief Problem Monitor (teacher and parent versions), the Wechsler Abbreviated Scale of Intelligence, and medical evaluation. The statistical analysis using a Confusion Matrix showed an accuracy of 82 and 95% for teacher and parent checklists, respectively. The decision-making model showed high indexes of sensitivity, providing evidence that teachers can be protagonists of the teaching-learning process mobilizing the parents to use the health care services.

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Section: Discussionmentioning

confidence: 99%

Section: Strategic Levelmentioning

confidence: 99%

DIagnosys: An Analytical Framework for the Identification of Elementary School Students with Intellectual Disability

et al. 2021

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“…The final strongest rules are in Group 7 which relate to (testBPS=PreHypn, fbs>120=FALSE, thal=FixedDefect) are extracted in (8,9,10,13,14,18) indicating their association to HeartDisease=yes. The confidence is (0.97, 0.95, 0.94, 0.90, 0.89, 0.83) which confirms the rules of Groups 3, 4 & 5, which have a strong relation between hypertension & diabetes with thalassemia.…”

Section: Mining Seci Model Secicarmentioning

confidence: 99%

“…In previous research, CARs have been used to demonstrate that classified rule patterns could be combined with a similar pattern, in the scope of; a colorectal cancer database [13], case-based reasoning. CARs have also been used in [14] to extract information concerning associations from a given case base using various approaches such as case-based reasoning [15,16]. Consequently, CAR as a DM tool can be used to discover significant relationships between risk factors implicated in target disease.…”

Section: Introductionmentioning

confidence: 99%

Pushing towards ehealth for iraqi hypertensives: an integrated class association rules into SECI model

Aljuboori

Tawfeeq

Al-Karawi

2021

IJEECS

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<span lang="EN-GB">This paper highlights the barriers that have led to a delay in the implementation of E-Health services in Iraq. A new framework is proposed to improve the E-Health sector using a SECI model which describes how explicit and tacit knowledge is generated, transferred, and recreated in organizations through main stages (socialization, externalization, combination and internalization). Class association rules (CARs) is integrated to mine the SECI model by extracting related rules which correspond to the medical advice. The proposed framework (SECICAR) can be done through a web portal to assemble healthcare professionals, patients in one environment. SECICAR will be applied to the hypertension community to show that disease if left untreated, frequently leads to serious illnesses such as heart disease. The SECICAR aims to facilitate the dissemination of tacit knowledge, which is explicit to hypertensives, in the form of strategies, guidelines and best practices. The validation of the SECICAR results displays satisfactory accuracy and reliability. Heuristic evaluation was used to test the web portal, the participants stating that there were no major issues regarding its usability.</span>

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“…Faia et al [11] used case-based reasoning, expert system, and collective intelligence to discuss issues related to energy reduction for building energy management systems. Aljuboori et al [12] proposed a case-based reasoning technique based on association rules to improve the performance of similarity-based retrieval and classed frequent pattern trees (FP-CAR) algorithms and to explore the technology and application of eliminating errors or ambiguous retrievals by case-based reasoning. Chung et al [13] developed a programming learning and diagnosis system built on case-based reasoning, which can provide feedback and suggestions to learners in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

A Web Services, Ontology and Big Data Analysis Technology-Based Cloud Case-Based Reasoning Agent for Energy Conservation of Sustainability Science

Chen

Yang

2020

Applied Sciences

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Energy conservation is one of the important topics for sustainability science, while case-based reasoning is one of the most important techniques for sustainable processing. This study aimed to develop a cloud case-based reasoning agent that integrates multiple intelligent technologies and supports, which can help users to quickly, accurately, and effectively obtain useful cloud energy-saving information in a timely manner for sustainability science. The system was successfully built with the support of Web services technology, ontology, and big data analytics. To set up this energy-saving case-based reasoning agent, this study reviewed the relevant technologies for building a web services platform and explored how to widely integrate and support the cloud interaction of the energy-saving data processing agent via the technologies. In addition to presenting relevant R&D technologies and results in detail, this study carefully conducted performance and learning experiments to prove the system’s effectiveness. The results showed that the core technology of the case-based reasoning agent achieved good performance and that the learning effectiveness of the overall system was also great.

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A New Strategy for Case-Based Reasoning Retrieval Using Classification Based on Association

Cited by 6 publications

References 37 publications

DIagnosys: An Analytical Framework for the Identification of Elementary School Students with Intellectual Disability

DIagnosys: An Analytical Framework for the Identification of Elementary School Students with Intellectual Disability

Pushing towards ehealth for iraqi hypertensives: an integrated class association rules into SECI model

A Web Services, Ontology and Big Data Analysis Technology-Based Cloud Case-Based Reasoning Agent for Energy Conservation of Sustainability Science

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