Enhancing Machine Learning Performance with Continuous In-Session Ground Truth Scores: Pilot Study on Objective Skeletal Muscle Pain Intensity Prediction

• URL: http://arxiv.org/abs/2308.00886v1
• Date: Wed, 2 Aug 2023 00:28:22 GMT
• Title: Enhancing Machine Learning Performance with Continuous In-Session Ground Truth Scores: Pilot Study on Objective Skeletal Muscle Pain Intensity Prediction
• Authors: Boluwatife E. Faremi, Jonathon Stavres, Nuno Oliveira, Zhaoxian Zhou and Andrew H. Sung
• Abstract summary: Machine learning models trained on subjective self-report scores struggle to objectively classify pain. This study developed two devices for acquisition of real-time, continuous in-session pain scores and gathering of ANS-modulated endodermal activity (EDA) Models trained with objective EDA features and in-session scores achieved superior performance (75.9% and 78.3%) compared to models trained with post-session scores (70.3% and 74.6%) respectively.
• Score: 0.31498833540989407
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Machine learning (ML) models trained on subjective self-report scores struggle to objectively classify pain accurately due to the significant variance between real-time pain experiences and recorded scores afterwards. This study developed two devices for acquisition of real-time, continuous in-session pain scores and gathering of ANS-modulated endodermal activity (EDA).The experiment recruited N = 24 subjects who underwent a post-exercise circulatory occlusion (PECO) with stretch, inducing discomfort. Subject data were stored in a custom pain platform, facilitating extraction of time-domain EDA features and in-session ground truth scores. Moreover, post-experiment visual analog scale (VAS) scores were collected from each subject. Machine learning models, namely Multi-layer Perceptron (MLP) and Random Forest (RF), were trained using corresponding objective EDA features combined with in-session scores and post-session scores, respectively. Over a 10-fold cross-validation, the macro-averaged geometric mean score revealed MLP and RF models trained with objective EDA features and in-session scores achieved superior performance (75.9% and 78.3%) compared to models trained with post-session scores (70.3% and 74.6%) respectively. This pioneering study demonstrates that using continuous in-session ground truth scores significantly enhances ML performance in pain intensity characterization, overcoming ground truth sparsity-related issues, data imbalance, and high variance. This study informs future objective-based ML pain system training.

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