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. 2021 Nov 25;60(1):249–261. doi: 10.1007/s11517-021-02467-y

Table 2.

Predicting intervention outcomes using data collected pre-intervention and a k-nearest neighbor model

Machine learning: k-nearest neighbor model
Feature sets All participants Unexpected non-responder
Outcome prediction for Outcome prediction for
ARAT BBT NHPT ARAT BBT NHPT
Balanced accuracy (%) Correct (yes/no)
1 55 63 43 y y y
2 49 28 54 n n y
3 52 37 43 y y y
4 43 53 40 n n y
5 77 66 71 y y y
6 80 63 64 n y n
1, 2 83 80 43 y y y
1, 3 60 25 50 y y y
1, 4 55 46 63 y y y
1, 5 69 55 64 y y y
1, 6 93 59 33 n y n
1, 2, 3 71 35 50 y y y
1, 4, 6 68 42 48 n y n
1, 5, 6 85 60 68 n y y
1, 4, 5, 6 64 69 61 n y n
1, 2, 3, 4, 5, 6 68 59 64 n y y

Multiple machine learning models were trained using different feature sets (independent variables, 1–6). The training label indicated whether a considerable change across intervention was observed in a specific conventional score (dependent variable; ARAT, BBT, or NHPT). The models were evaluated in a leave-one-out cross-validation and specifically tested for one individual with strong activity limitations who did not show improvements across neurorehabilitation (referred to as unexpected non-responder). Feature set nomenclature: (1) patient master data (ms type, chronicity, age, sex); (2) intervention group; (3) disability (EDSS, disability group); (4) conventional scales of body functions (motricity index, static fatigue index, monofilament index, symbol digit modality test, Fahn’s tremor rating scale); (5) digital health metrics of sensorimotor impairments (ten VPIT metrics); (6) Conv. scale of activity (ARAT, NHPT, BBT). The best performing (accuracy and unexpected non-responder) models relying on the least amount of features are highlighted in bold for each conventional scale. ARAT: Action Research Arm Test. BBT: Box and Block Test. NHPT: Nine Hole Peg Test. VPIT: Virtual Peg Insertion Test