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

By Nicoletta Prentzas, Andrew Nicolaides, E. Kyriacou, A. Kakas, Constantinos S. Pattichis. 2019

47 citations recorded by the source corpus.

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].

Subfields: Artificial Intelligence, Health Information Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Umit Cali
2. Cuntai Guan
3. Vinayak Sharma
4. Jun Xia
5. Özgür Güler
6. Guang Yang
7. Murat Kuzlu
8. Qinghao Ye

In Health Information Management

1. Cuntai Guan
2. Jun Xia
3. Guang Yang
4. Qinghao Ye
5. Erico Tjoa

Citations in the corpus

• A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, methodology): “brain, neuro* breast [68], lung [6], [81], [47], [67], [130], [152] sleep [153], skin [154] [131], [132], [135], [155] others [105] Method.”
• Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond (2021, methodology): “[151] implemented the argumentation theory for their XAI algorithm training process by extracting decision rules.”
• Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools (2020, background): “XAI has been explored more in areas where explainability and transparency of the model’s working is critical, such as healthcare-stroke detection [13], cybersecurity-Intrusion Detection Systems (IDS) [14], military-target classification [15], and finance-risk management [16].”