Table 7.

A summary of some case-based reasoning (CBR) models and framework, and their domains of application, approaches/techniques used, description of approach and accuracy of the systems.

Studies [Ref]	Year	Approach used for reasoning or diagnoses	Domain of Application	Accuracy (%)
Proposed framework	2020	CBR and NLP, and Semantic Web	Detection and diagnosis of COVID-19 (Novel Coronavirus)	94.54
Rahim et al. [44]	2019	Traditional CBR	Diagnosis of psychological disorders	–
Zhong et al. [45]	2018	Text-CBR and ontology	Non-medical: Fault diagnosis and predication by cloud computing	–
Zhang et al. [46]	2017	Traditional CBR	Non-medical: Theory of inventive problem solving for inventive design	–
El-Sappagh et al. [42]	2015	Fuzzy-CBR, and Ontologies	Diabetics	97.67
Shen et al. [47]	2015	CBR with ontology approach	Diagnosis of gastric cancer	–
Heras et al. [43]	2013	CBR with ontology approach	Non-medical: multi-agent systems	–
Li and Ho [48]	2009	CBR and fuzzy logic	Non-medical: Prediction of financial activity	92.36
Petrovic et al. [49]	2011	Traditional CBR	Radiotherapy planning	84.72
Fan et al. [50]	2009	CBR, Fuzzy decision tree	Medical data classification: breast cancer and liver disorders	98.40 and 81.60
Begum et al. [51]	2009	CBR and fuzzy logic	Medical data for diagnosis of stress	90.00