Enhancing Schizophrenia Prediction Using Class Balancing and SHAP Explainability Techniques on EEG Data
Author:
Affiliation:
1. University of Talca,PhD in Engineering Systems,Curicó,Chile
2. Universidad de Talca,Faculty of Engineering,Department of Computer Science,Curicó,Chile
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10179008/10178988/10179002.pdf?arnumber=10179002
Reference21 articles.
1. Improved Multiclassification of Schizophrenia Based on Xgboost and Information Fusion for Small Datasets
2. Emotion Recognition of EEG Signals Based on the Ensemble Learning Method: AdaBoost
3. Diagnostic value of structural and diffusion imaging measures in schizophrenia
4. Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features
5. Gray matter volume drives the brain age gap in schizophrenia: a SHAP study
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. An interpretable schizophrenia diagnosis framework using machine learning and explainable artificial intelligence;Systems Science & Control Engineering;2024-06-27
2. A Novel Approach for Classification of Proneness of Schizophrenia in Patients using Ensemble Machine Learning Techniques and LIME;2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN);2024-05-03
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