TY  - JOUR
T1  - Predicting Significant Blood Marker Values for Pressure Ulcer Forecasting Utilizing Feature Minimization and Selection
AU  - Kim, Yeonhee 
AU  - Jung, Hoyoul 
AU  - Choi, Jang-Hwan 
JO  - Journal of KIISE, JOK
PY  - 2023
DA  - 2023/1/14
DO  - 10.5626/JOK.2023.50.12.1054
KW  - spinal cord injury
KW  - correlation
KW  - machine learning
KW  - pressure ulcers
KW  - LSTM
KW  - clinical features
AB  - Pressure ulcers are difficult to treat once they occur, and huge economic costs are incurred during the treatment process. Therefore, predicting the occurrence of pressure ulcers is important in terms of patient suffering and economics. In this study, the correlation between the lab codes (features) and pressure ulcers obtained from blood tests of patients with spinal cord injury was analyzed to provide meaningful characteristic information for the prediction of pressure ulcers. We compare and analyze the correlation coefficients of Pearson, Spearman, and Kendall"s tau, which are mainly used in feature selection methods. In addition, the importance of features is calculated using XGBoost and LightGBM, which are machine learning methods based on gradient boosting. In order to verify the performance of this model, we use the long short-term memory (LSTM) model to predict other features using the features occupying the top-5 in importance. In this way, unnecessary features can be minimized in diagnosing pressure ulcers and guidelines can be provided to medical personnel.