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Table 4 Synergy analysis in PVBSP forecasting

From: Single nucleotide polymorphism genes and mitochondrial DNA haplogroups as biomarkers for early prediction of knee osteoarthritis structural progressors: use of supervised machine learning classifiers

Scenario 1
Model Input combinations Accuracy (%)
Train Test
Model 1 Equation 1 (12 variables) 83.2 88.8
M31 Equation 2 (5 variables) 75.5 78.8
M32-1 M31 + GNL3 81.4 82.7
M32-2 M31 + MCF2L 77.0 81.3
M32-3 M31 + FTO 82.7 85.0
Scenario 2
No. of inputs Model no Accuracy (%)
Train Test
(3 + 1) variables MH2 75.5 80.0
Age, BMI, mtDNA haplogroup, FTO
(3 + 2) variables MH17 85.2 82.5
Age, BMI, mtDNA haplogroup, FTO, SUPT3H
(3 + 3) variables MH46 82.1 88.8
Age, BMI, mtDNA haplogroup, FTO, SUPT3H, GNL3
(3 + 4) variables MH80 82.7 88.8
Age, BMI, mtDNA haplogroup, TP63, DUS4L, GDF5, TGFA
(3 + 5) variables MH101 83.2 88.8
Age, BMI, mtDNA haplogroup, TP63, GNL3, DUS4L, GDF5, TGFA
(3 + 6) variables MH106 83.2 88.8
Age, BMI, mtDNA haplogroup, TP63, FTO, SUPT3H, GNL3, DUS4L, GDF5
  1. Model 1 is Eq. 1: PVBSP = f(age, BMI, mtDNA haplogroup, cluster, TP63, FTO, GNL3, DUS4L, GDF5, SUPT3H, MC2FL, TGFA), and M31, Eq. 2: PVBSP = f(age, BMI, TP63, DUS4L, GDF5)
  2. M Model, No. Number, PVBSP Probability values of being structural progressors, test Testing stage, train Training stage