Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/30046| Title: | Integrating machine learning with symbolic reasoning to build an explainable ai model for stroke prediction | Authors: | 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 | Keywords: | Argumentation;Explainability;InTrees;Random forests;XAI | Issue Date: | 28-Oct-2019 | Source: | 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 | Abstract: | 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 |
| Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
CORE Recommender
SCOPUSTM
Citations
20
37
checked on Mar 14, 2024
Page view(s)
127
Last Week
2
2
Last month
4
4
checked on Dec 22, 2024
Google ScholarTM
Check
Altmetric
This item is licensed under a Creative Commons License
