Name
Title | ||
|---|---|---|
Integrating Machine Learning With Symbolic Reasoning To Build An Explainable Ai Model For Stroke Prediction |
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]. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BIBE.2019.00152 | 2019 IEEE 19TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE) |
Keywords | Field | DocType |
argumentation, explainability, inTrees, random forests, XAL | Black box (phreaking),Symbolic reasoning,Computer science,Argumentation theory,Artificial intelligence,Svm classifier,Random forest,Machine learning | Conference |
ISSN | Citations | PageRank |
2471-7819 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicoletta Prentzas | 1 | 0 | 0.68 |
| A. Nicolaides | 2 | 72 | 7.46 |
| Efthyvoulos Kyriacou | 3 | 0 | 0.34 |
| Antonis C. Kakas | 4 | 0 | 0.68 |
| Constantinos S. Pattichis | 5 | 382 | 43.89 |