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https://hdl.handle.net/20.500.14279/30046| Τίτλος: | Integrating machine learning with symbolic reasoning to build an explainable ai model for stroke prediction | Συγγραφείς: | Prentzas, Nicoletta Nicolaides, Andrew N. Kyriacou, Efthyvoulos C. Kakas, Antonis Pattichis, Constantinos S. |
Major Field of Science: | Engineering and Technology | Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering | Λέξεις-κλειδιά: | Argumentation;Explainability;InTrees;Random forests;XAI | Ημερομηνία Έκδοσης: | 28-Οκτ-2019 | Πηγή: | 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019, Athens,28 - 30 October 2019 | Start page: | 817 | End page: | 821 | Conference: | Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019 | Περίληψη: | Despite the recent recognition of the value of Artificial Intelligence and Machine Learning in healthcare, barriers to further adoption remain, mainly due to their 'black box' nature and the algorithm's inability to explain its results. In this paper we present and propose a methodology of applying argumentation on top of machine learning to build explainable AI (XAI) models. We compare our results with Random Forests and an SVM classifier that was considered best for the same dataset in [1]. | URI: | https://hdl.handle.net/20.500.14279/30046 | ISBN: | 9781728146171 | DOI: | 10.1109/BIBE.2019.00152 | Rights: | © IEEE Attribution-NonCommercial-NoDerivatives 4.0 International |
Type: | Conference Papers | Affiliation: | University of Cyprus Frederick University |
| Εμφανίζεται στις συλλογές: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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