1. Ktisis Cyprus University of Technology
  2. Cyprus University of Technology (Research Output)
  3. Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο: 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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