Optimising retrieval phase in CBR through Pearl and JLO algorithms for medical diagnosis

Djebbar, Akila; Merouani, Hayet Farida

doi:10.1504/ijaip.2013.056420

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Moreover, BNs can diagnose diseases with minimal human intervention, even in mild cases, and their integration into electronic medical record systems could aid in recognising underdiagnosed disorders. Djebbar et al's findings demonstrate that combining BNs with case-based reasoning enhances the efficiency of case retrieval in liver disease diagnosis [56].…”

Section: Discussionmentioning

confidence: 99%

“…Unexpected interactions between thermographic attributes, especially those directly related to the class variable, were shown by the models. BNs were used by Djebbar and Merouani [56] to model case-based reasoning and apply it to the diagnosis of liver disease. It was found that the BN was an excellent tool for the modelling of uncertainty in terms of its clear graphical representation and the laws of conditional probability based on the similarity function, and the BN was useful in selecting the most similar cases as the recovery phase and optimising it.…”

Section: Comprehensive Review Of Bayesian Network On Some Diseasesmentioning

confidence: 99%

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Bayesian Networks for the Diagnosis and Prognosis of Diseases: A Scoping Review

Polotskaya,

Muñoz-Valencia,

Rabasa

et al. 2024

MAKE

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Bayesian networks (BNs) are probabilistic graphical models that leverage Bayes’ theorem to portray dependencies and cause-and-effect relationships between variables. These networks have gained prominence in the field of health sciences, particularly in diagnostic processes, by allowing the integration of medical knowledge into models and addressing uncertainty in a probabilistic manner. Objectives: This review aims to provide an exhaustive overview of the current state of Bayesian networks in disease diagnosis and prognosis. Additionally, it seeks to introduce readers to the fundamental methodology of BNs, emphasising their versatility and applicability across varied medical domains. Employing a meticulous search strategy with MeSH descriptors in diverse scientific databases, we identified 190 relevant references. These were subjected to a rigorous analysis, resulting in the retention of 60 papers for in-depth review. The robustness of our approach minimised the risk of selection bias. Results: The selected studies encompass a wide range of medical areas, providing insights into the statistical methodology, implementation feasibility, and predictive accuracy of BNs, as evidenced by an average area under the curve (AUC) exceeding 75%. The comprehensive analysis underscores the adaptability and efficacy of Bayesian networks in diverse clinical scenarios. The majority of the examined studies demonstrate the potential of BNs as reliable adjuncts to clinical decision-making. The findings of this review affirm the role of Bayesian networks as accessible and versatile artificial intelligence tools in healthcare. They offer a viable solution to address complex medical challenges, facilitating timely and informed decision-making under conditions of uncertainty. The extensive exploration of Bayesian networks presented in this review highlights their significance and growing impact in the realm of disease diagnosis and prognosis. It underscores the need for further research and development to optimise their capabilities and broaden their applicability in addressing diverse and intricate healthcare challenges.

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Section: Discussionmentioning

confidence: 99%

Section: Comprehensive Review Of Bayesian Network On Some Diseasesmentioning

confidence: 99%