Integrating Machine Learning with Symbolic Reasoning to Build an Explainable AI Model for Stroke Prediction

Nicoletta Prentzas,Antonis Kakas,Efthyvoulos Kyriacou,Andrew Nicolaides,Constantinos Pattichis

doi:10.1109/bibe.2019.00152

Integrating Machine Learning with Symbolic Reasoning to Build an Explainable AI Model for Stroke Prediction

Nicoletta Prentzas, Antonis Kakas + Show 3 more

https://doi.org/10.1109/bibe.2019.00152

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Publication Date: Oct 1, 2019

Citations: 31

Affiliation: University of Cyprus, Frederick University

#Explainable AI Model #Machine Learning In Healthcare + Show 8 more

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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].

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