Machine-learning-based investigation on classifying binary and multiclass behavior outcomes of children with PIMD/SMID
This work addresses the challenge of predicting behavior for independent communication and mobility in children with profound intellectual and multiple disabilities, representing an incremental improvement by applying existing methods to new data in a specific domain.
This study tackled the problem of classifying behavior outcomes of children with PIMD/SMID by investigating whether recalibrating datasets with location and weather data, combined with feature selection, improves classification accuracy for binary and multiclass outcomes using machine learning classifiers like XGB, SVM, RF, and NN.
Recently, the importance of weather parameters and location information to better understand the context of the communication of children with profound intellectual and multiple disabilities (PIMD) or severe motor and intellectual disorders (SMID) has been proposed. However, an investigation on whether these data can be used to classify their behavior for system optimization aimed for predicting their behavior for independent communication and mobility has not been done. Thus, this study investigates whether recalibrating the datasets including either minor or major behavior categories or both, combining location and weather data and feature selection method training (Boruta) would allow more accurate classification of behavior discriminated to binary and multiclass classification outcomes using eXtreme Gradient Boosting (XGB), support vector machine (SVM), random forest (RF), and neural network (NN) classifiers. Multiple single-subject face-to-face and video-recorded sessions were conducted among 20 purposively sampled 8 to 10 -year old children diagnosed with PIMD/SMID or severe or profound intellectual disabilities and their caregivers.