Machine-learning-based investigation on classifying binary and multiclass behavior outcomes of children with PIMD/SMID

TL;DR

This study explores how weather, location, and behavior data can be used to classify behaviors of children with PIMD/SMID, aiming to improve system predictions for communication and mobility.

Contribution

It introduces a novel approach combining weather, location, and behavior data with feature selection to enhance behavior classification accuracy in children with PIMD/SMID.

Findings

Recalibrated datasets improve classification accuracy.

Feature selection with Boruta enhances model performance.

Multiple classifiers achieve high accuracy in behavior prediction.

Abstract

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.