Key result
XGBoost model using EEG data predicts schizophrenia with ~93% accuracy, outperforming SVM and AdaBoost.
Why the study?
Complex machine learning models lack transparency, reducing confidence in health-related predictions such as schizophrenia detection from EEG data.
Does the XGBoost model outperform SVM and AdaBoost in predicting schizophrenia using EEG data?
Does the XGBoost model outperform SVM and AdaBoost in predicting schizophrenia using EEG data?
An XGBoost machine learning model applied to EEG data accurately predicts schizophrenia and identifies specific delta wave alterations as key predictive features.
EEG-based XGBoost may aid schizophrenia prediction; leaves open prospective validation before clinical use.
Machine learning (ML) makes predictions or supports decision making based on data, achieving high accuracy, saving time and resources, and even running real-time analysis. However, one drawback of these models is the lack of transparency in complex models, reducing confidence in sensitive fields such as health. This paper analyzes electroencephalogram (EEG) data to predict schizophrenia in patients. Three classifiers are compared, considering Support Vector Machines(SVM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Three metrics are used to measure the classification process, including accuracy (ACC), area under the curve (AUC) and F-l score (F1). XAI is incorporated into the pipeline to identify relevant features. XGBoost is the model with the best performance in predicting schizophrenia cases, reaching an ACC = 0.93, AUC = 0.93 and F1 score = 0.92, outperforming the SVM and AdaBoost algorithms. The SHAP explainability technique was applied on the XGBoost model, identifying the sex, IQ, delta T6, and delta Pz waves as the most relevant characteristics in the prediction processes. Based on the data analysis, we found that schizophrenia causes an alteration of the delta wave in an EEG, which is different to other mental illnesses. On the other hand, the study generates a specific impact by showing that XGBoost presents better results in 3 validation metrics (ACC, AUC, and F1 Score) compared to SVM and AdaBoost. It is shown that balancing classes is essential to obtain better ML predictions.
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Arias et al. (2023) studied Schizophrenia. XGBoost machine learning model vs. Support Vector Machines (SVM) and Adaptive Boosting (AdaBoost) was evaluated on Schizophrenia prediction performance (Accuracy, AUC, F1 score). An XGBoost machine learning model using EEG data predicted schizophrenia with an accuracy of 0.93, AUC of 0.93, and F1 score of 0.92, outperforming SVM and AdaBoost algorithms.
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