Table 3 Main study: Mean ± standard deviation of classification performance metrics over the 5-fold model evaluation CV with features separately computed over the Interview and Mental Arithmetics phases of the (f-)TSST, respectively.

From: Machine learning-based detection of acute psychosocial stress from body posture and movements

Scaler

Feature selection

Classifier

Accuracy [%]

F1-score [%]

Precision [%]

Min-Max

SFM

RF

73.4 (7.7)

71.7 (9.7)

75.4 (7.6)

Min-Max

SkB

DT

70.7 (8.8)

69.9 (12.5)

70.3 (8.8)

Min-Max

SkB

NB

68.0 (6.3)

64.2 (10.1)

71.6 (6.6)

Standard

SkB

MLP

68.0 (6.3)

63.2 (13.8)

71.2 (2.7)

Standard

SkB

kNN

67.0 (6.3)

59.1 (9.9)

76.0 (5.6)

Min-Max

RFE

SVM

66.8 (5.4)

64.3 (8.7)

68.3 (3.3)

Standard

RFE

Ada

63.0 (6.3)

63.5 (3.8)

64.5 (9.7)

  1. For each evaluated classifier, the classification pipeline combination with the highest mean accuracy is shown. The classification pipelines scoring the highest metrics are highlighted in bold.