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Table 2 Predictive performance on hypertension dataset

From: Leveraging auxiliary measures: a deep multi-task neural network for predictive modeling in clinical research

Dataset

Model

KNN

RF

SVR

Ridge

Lasso

MTLasso

MLP-4

GATAN-1

GATAN-2

Full feature set

MSE

248.06

214.68

299.03

261.52

205.67

217.34

209.43

199.76

203.50

  

(60.73)

(25.18)

(82.16)

(23.26)

(36.07)

(39.35)

(28.36)

(33.48)

(29.98)

 

EVS

0.26

0.29

0.08

0.10

0.33

0.30

0.32

0.36

0.34

  

(0.18)

(0.12)

(0.02)

(0.37)

(0.11)

(0.14)

(0.14)

(0.10)

(0.14)

 

MAE

10.91

11.29

11.66

12.41

11.40

11.65

10.43

10.20

10.77

  

(2.05)

(1.97)

(1.93)

(1.65)

(2.58)

(2.53)

(2.02)

(1.71)

(2.10)

Lab and demo

MSE

282.06

261.27

284.05

278.80

250.754

253.59

243.41

237.97

237.66

  

(39.58)

(20.56)

(58.15)

(18.88)

(26.01)

(33.79)

(31.87)

(33.59)

(34.09)

 

EVS

0.06

0.08

0.06

0.03

0.15

0.14

0.17

0.19

0.19

  

(0.17)

(0.25)

(0.01)

(0.22)

(0.11)

(0.11)

(0.10)

(0.09)

(0.10)

 

MAE

10.54

10.42

9.90

10.24

9.59

9.43

8.84

8.67

8.54

  

(2.38)

(0.95)

(1.24)

(1.78)

(1.26)

(0.94)

(1.96)

(2.05)

(2.01)

  1. The first section uses a full set of features; the second only uses lab results and demographic information
  2. The best performance is bolded