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A quality model for the evaluation of decision support systems based on a knowledge discovery from data process

Abstract : Providing final users with confident knowledge to help them make the right decisions is the goal of using a decision support system based on a knowledge discovery from data process (DSS/KDD). Some failures can be found in such systems especially when the mined knowledge is unconfident, or if the system is hardly usable. The objective of this study is to define a quality model (QM) which ensures a global evaluation of DSS/KDD that generates association rules. The proposed QM evaluates the DSS/KDD regarding three dimensions: utility, usability and interestingness. It defines a set of criteria and allows the measurement of a DSS/KDD quality evaluation. To validate the proposed approach, a prototype has been developed. Weka and a DSS/KDD in the healthcare domain were assessed drawing on 20 users who participated in the evaluation process. Results have shown that a user-centred QM leads to a better quality of such systems
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https://hal-uphf.archives-ouvertes.fr/hal-03466801
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Submitted on : Monday, December 6, 2021 - 11:11:59 AM
Last modification on : Tuesday, December 7, 2021 - 3:48:36 AM

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Emna Ben Ayed, Mounir Ben Ayed. A quality model for the evaluation of decision support systems based on a knowledge discovery from data process. Journal of Decision Systems, Abingdon Oxfordshire UK: Routledge Taylor & Francis Group, 2016, 25 (2), pp.95-117. ⟨10.1080/12460125.2016.1156999⟩. ⟨hal-03466801⟩

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