Predicting the Anxiety of Patients with Alzheimer’s Dementia using Boosting Algorithm and Data-Level Approach

Since overfitting due to imbalanced data can cause prediction errors during the learning process of machine learning and degrades the prediction performance of the model (e.g., sensitivity), it is necessary to add an additional data sampling technique in the model development step to reduce overfitting to overcome this issue, in addition to selecting a machine learning algorithm suitable for the data. This study examined Alzheimer's patients living in South Korea to understand the predictors of anxiety using boosting algorithms (i.e., AdaBoost and XGBoost) and data-level approach (raw data, undersampling, oversampling, and SMOTE) and confirmed the machine learning algorithm with the best prediction performance. We analyzed 253 elderly people who were diagnosed with Alzheimer's disease (aged from 60 to 74 years old) who visited rehabilitation hospitals for early dementia screening. This study developed models for predicting the anxiety of Alzheimer's dementia patients using AdaBoost and XGBoost. Moreover, this study compared the prediction performance (i.e., accuracy, sensitivity, and specificity) of the models. The results of this study showed that XGBoost based on SMOTE (accuracy=0.84, sensitivity=0.85, and specificity=0.81) was identified as the model with the best prediction performance. Consequently, the results of this study presented that using a SMOTE-XGBoost model may provide higher accuracy than using a SMOTE-Adaboost model for developing a prediction model using outcome variable imbalanced data such as disease data in the future.

@article{Byeon2021,
  title     = {Predicting the Anxiety of Patients with Alzheimer’s Dementia using Boosting Algorithm and Data-Level Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {3},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Haewon Byeon},
  doi       = {10.14569/IJACSA.2021.0120313},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120313}
}