Explainable Chronic Kidney Disease (CKD) Prediction using Deep Learning and Shapley Additive Explanations (SHAP)
Author:
Affiliation:
1. Department of Software and Information Systems, University of Mauritius, Mauritius
2. Department of Nephrology, Sir Seewoosagur Ramgoolam National (SSRN) Hospital, Mauritius
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3633598.3633604
Reference27 articles.
1. Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models
2. Liat Antwarg, Ronnie Mindlin Miller, Bracha Shapira, and Lior Rokach. 2021. Explaining anomalies detected by autoencoders using Shapley Additive Explanations. Expert systems with applications 186 (2021), 115736.
3. Anthony C Atkinson Marco Riani and Aldo Corbellini. 2021. The box–cox transformation: Review and extensions. (2021).
4. Anusorn Charleonnan, Thipwan Fufaung, Tippawan Niyomwong, Wandee Chokchueypattanakit, Sathit Suwannawach, and Nitat Ninchawee. 2016. Predictive analytics for chronic kidney disease using machine learning techniques. In 2016 management and innovation technology international conference (MITicon). IEEE, MIT–80.
5. Deep neural network using trainable activation functions
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