Table 3.

Data sources and size, ML methods, and percentage accuracy

Study	Data input	Data sources/instruments	ML methods	ML methods’ accuracy (%)	Sensitivity	Specificity	Validation	Number of classes
Abdollahi 2020 [9]	4	Kinematics, inertial measurement units—range of movement, balance, self-reported data	SVM, MLP	SVM = 75%, MLP = 60%	SVM = 59% MLP = 51%	SVM = 52% MLP = 54%	Leave-one-participant-out cross-validation	3 classes classification of pain risk groups: low, medium, and high
Darvishi 2017[10]	4	Self-reported data	kNN, LoR, NN	kNN = 81%, LoR = 83%, NN = 92%	Not reported	Not reported	Training/testing	2 classes of “healthy individuals” and “individuals with LBP”
Grauhan 2021[11]	6	Radiography data	CNN	CNN = 81%	CNN = 80%	CNN = 82%	Training/testing	4 classes of shoulder pain: osteoarthritis; calcification; dislocation; fracture
Lee 2019 [12]	7	Functional magnetic resonance imaging (fMRI)	SVM	SVM = 81%	SVM = 50%	SVM = 50%	Leave-one-patient-out cross-validation	2 classes of relatively lower pain and higher pain
Liew 2020 [13]	10	Electromyographic (EMG), kinematics	FD Boost	FDBoost = 92%	Not reported	Not reported	Cross-validation	Numerical rating scale (NRS) (0 no pain, 10 being maximal pain)
Miettinen 2021 [14]	6	Self-reported data	CART, PART, RF	CART = 80%, PART = 80%, RF = 81%	CART = 63% PART = 56% RF = 66%	CART = 83% PART = 92% RF = 85%	Cross validation	3 pain phenotype classes: lowest pain intensity and pain interference; moderate pain intensity and pain interference; high pain intensity and pain interference
Rahman 2018 [15]	13	Self-reported data	LASSO, LoR, RF, SVM	LASSO = 72%, LoR = 69%, RF = 70%, SVM = 63%	Not reported	Not reported	Fivefold cross-validation	2 classes of low and high pain volatility
Santana 2019 [16]	21	fMRI data	CNN	CNN = 87%	Not reported	Not reported	k-fold cross-validation	2 classes of yes or no chronic pain
Santra 2020 [17]	15	Self-reported data	Bayes	Bayes = 71%	Not reported	Not reported	Training/testing	5 classes of different LBP: SIJA (pain from sacroiliac joint); FJA (pain from lumbar facet joints); DP (pain from the disc); PIVD (pain from the nerve roots adjacent to vertebral body); PS (myofascial pain from piriformis muscle)
Snyder 2021 [18]	6	Inertial measurement unit sensors	CNN	CNN = 91%	Not reported	Not reported	Cross-validation	3 classes of low, medium and high risk of back injury
Ahn 2018 [19]	4	NeuroSensory analyzer, pressure algometer	Bayes	Bayes = 75%	Not reported	Not reported	Training/testing	2 classes of higher experimental pain sensitivity and lower experimental pain sensitivity
Fernandes 2017 [20]	6	Self-reported data	Bayes	Bayes = 70%	Bayes = 95%	Bayes = 32%	Testing	2 classes of knee pain and no knee pain
Kimura 2021 [21]	9	Electroencephalography (EEG), self-reported data	SVM	SVM = 80%	Not reported	Not reported	Testing	2 classes of hip pain and no hip pain
Lotsch 2020 [22]	12	Erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), swollen joint count (SJC), tender joint count (TJC), self-reported data	Bayes, CART, kNN, MLP, SVM,	Bayes = 70%, CART = 71%, kNN = 72%, MLP = 70%, SVM = 70%	Bayes = 36%, CART = 43%, kNN = 45%, MLP = 31%, SVM = 39%	Bayes = 78%, CART = 73%, kNN = 70%, MLP = 87%, SVM = 77%	Nested cross-validation analysis	3 classes of low, moderate, and high pain score
Parthipan 2019 [23]	6	Self-reported data	EN	EN = 92%	Not reported	Not reported	Tenfold cross-validation	Numerical rating scale (NRS) (0 no pain, 10 being maximal pain)
Tighe 2015 [24]	14	Self-reported data	GB, LASSO, kNN	GB = 67%, LASSO = 71%, kNN = 64%	LASSO = 68%,	LASSO = 61%	Training/testing	2 classes of moderate and severe postoperative pain
Gruss 2015 [25]	42	EMG, skin conductance level (SCL), EEG	SVM	SVM = 85%	SVM = 83%	SVM = 85%	Threefold cross-validation	2 classes of pain and no pain
Levitt 2020 [26]	39	EEG	SVM	SVM = 83%	Not reported	Not reported	Tenfold cross-validation	2 classes of healthy participants and participants with pain
Pouromran 2021 [27]	4	Electrodermal activity (EDA), ECG, EMG	kNN, LiR, NN, SVM, XGBoost	kNN = 81%, LiR = 81%, NN = 79%, SVM = 83%, XGBoost = 81%	Not reported	Not reported	Testing	Numeric scale from 0 to 4 for pain intensity
Juwara 2020 [28]	8	Self-reported data	EN, GB, LS, RF, RR	EN = 74%, GB = 78%, LS = 72%, RF = 73%, RR = 75%	Not reported	Not reported	Tenfold cross-validation	2 classes of acute pain and not acute pain
Rojas-Mendizabal 2021[29]	27	Self-reported data	DTs, kNN, LoR, RF, SVM,	DTs = 79%, kNN = 71%, LoR = 96% RF = 81%, SVM = 81%	Not reported	Not reported	Tenfold cross-validation	2 classes of cardiac pain and non-cardiac pain
Rogachov 2018 [30]	4	fMRI	KRR	Not reported	Not reported	Not reported	Cross-validation	Numerical rating scale (NRS) (0 no pain, 10 being maximal pain)
Tan 2020 [31]	15	Electronic health record	LoR, RF, XGBoost,	LoR = 77%, RF = 76%, XGBoost = 76%	LoR = 69%, RF = 69%, XGBoost = 67%	LoR = 73%, RF = 71%, XGBoost = 76%	Fivefold cross-validation	2 classes of pain and no pain
Yang 2018 [32]	11	Electronic health records	kNN, LoR, RF, SVM	kNN = 52%, LoR = 58%, RF = 52%, SVM = 58%,	Not reported	Not reported	Tenfold cross-validation	Numerical rating scale (NRS) (0 no pain, 10 being maximal pain)
Wang et al. 2021 [34]	7	Self-reported data	DTs, SVM, CNN	DTs = 90%	Not reported	Not reported	Tenfold cross-validation	Numerical rating scale (NRS) (0 no pain, 10 being maximal pain)
Goldstein et al. 2020 [33]	12	Mobile platform	LiR	LiR = 72%	Not reported	Not reported	k-fold cross-validation	Numerical rating scale (NRS) (0 no pain, 10 being maximal pain)

SVM support vector machine, MLP multilayer perceptron, kNN k-nearest neighbors, LoR logistic regression, NN neural network, CNN convolutional neural network, FD Boost functional data boost, CART classification and regression trees, PART partial decision trees, RF random forest, LASSO least absolute shrinkage and selection operator, GB gradient boosting, LiR linear regression, XGBoost extreme gradient boost, EN elastic net, LS least square, RR ridge regression, DTs decision trees, KRR kernel ridge regression