A classification algorithm to predict chronic pain using both regression and machine learning - A stepwise approach

Abstract

This secondary data analysis study aimed to (1) investigate the use of two sense-based parameters (movement and sleep hours) as predictors of chronic pain when controlling for patient demographics and depression, and (2) identify a classification model with accuracy in predicting chronic pain. Data collected by Oregon Health & Science University between March 2018 and December 2019 under the Collaborative Aging Research Using Technology Initiative were analyzed in two stages. Data were collected by sensor technologies and questionnaires from older adults living independently or with a partner in the community. In Stage 1, regression models were employed to determine unique sensor-based behavioral predictors of pain. These sensor-based parameters were used to create a classification model to predict the weekly recalled pain intensity and interference level using a deep neural network model, a machine learning approach, in Stage 2. Daily step count was a unique predictor for both pain intensity (75% Accuracy, F1 = 0.58) and pain interference (82% Accuracy, F1 = 0.59). The developed classification model performed well in this dataset with acceptable accuracy scores. This study demonstrated that machine learning technique can be used to identify the relationship between patients' pain and the risk factors.

Keywords: Depression; Machine learning; Pain; Physical activity; Sleep.

Copyright © 2021 Elsevier Inc. All rights reserved.

Figures

Figure 1.

LSTM-based Prediction Model for Level of Pain Intensity