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Neural network diagnostic system for dengue patients risk classification

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Abstract

With the dramatic increase of the worldwide threat of dengue disease, it has been very crucial to correctly diagnose the dengue patients in order to decrease the disease severity. However, it has been a great challenge for the physicians to identify the level of risk in dengue patients due to overlapping of the medical classification criteria. Therefore, this study aims to construct a noninvasive diagnostic system to assist the physicians for classifying the risk in dengue patients. Systematic producers have been followed to develop the system. Firstly, the assessment of the significant predictors associated with the level of risk in dengue patients was carried out utilizing the statistical analyses technique. Secondly, Multilayer perceptron neural network models trained via Levenberg-Marquardt and Scaled Conjugate Gradient algorithms was employed for constructing the diagnostic system. Finally, precise tuning for the models’ parameters was conducted in order to achieve the optimal performance. As a result, 9 noninvasive predictors were found to be significantly associated with the level of risk in dengue patients. By employing those predictors, 75% prediction accuracy has been achieved for classifying the risk in dengue patients using Scaled Conjugate Gradient algorithm while 70.7% prediction accuracy were achieved by using Levenberg-Marquardt algorithm.

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Acknowledgment

This work is financially supported by a Malaysian Ministry of Science Technology and Innovation (MOSTI) Science Fund Project No. 11-02-03-1014 and postgraduate research Fund (PPP) No. PS138-2008B, University of Malaya.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia

    Tarig Faisal & Fatimah Ibrahim

  2. Faculty of Electrical Engineering, Universiti Teknologi Mara, 40450, Shah Alam, Selangor, Malaysia

    Mohd Nasir Taib

Authors
  1. Tarig Faisal
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  2. Mohd Nasir Taib
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  3. Fatimah Ibrahim
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Corresponding author

Correspondence to Tarig Faisal.

Appendix

Appendix

Simple logistic regression

  • ➢ Null hypothesis states that there will be no relationship between the probability and the independent variable.

  • ➢ The -2Loglikelihood vale for each variable was tested with a null model.

  • ➢ The significant variable was selected according the chi-square value at significant level of 0.25

Null model

No

Variables

B

Significance(sig.)

-2 Loglikelihood

1

constant

−0.22

0.015

694.076

Significant at p = 0.25 (1DF: chi-square 1.323)

Variables

B

-2 Loglikelihood

G-value

Gander

−0.317

690.991

3.086

 

Weight(w)

0.026

672.584

21.493

 

Phase Angle (PA)

−0.028

693.964

0.113

*

Body Capacitance (BC)

0.001

689.308

4.768

 

Resistance (RES)

−0.005

667.485

26.592

 

Reactance (REACT)

−0.032

675.590

18.487

 

Extracellular Mass (ECM)

−0.066

674.458

19.618

 

Body Cell Mass (BCM)

−0.036

688.238

5.839

 

Lean body mass (LBM)

−0.033

680.855

13.222

 

Fat Mass (FM)

0.033

680.855

13.222

 

(ERB) = (ECM/BCM)

1.560

684.763

9.313

 

Body Mass Index (BMI)

0.119

647.612

46.465

 

Basal Metabolic Rate (BMR)

0.001

687.165

6.912

 

Total Body Water (TBW)

0.022

688.420

5.657

 

TRT = TBW/W

−0.037

683.804

10.272

 

Extracellular Water (ECW)

0.071

677.375

16.702

 

Intracellular Water (ICW)

−0.071

677.341

16.735

 

ERI = ECW/ICW

2.329

671.340

22.736

 

DAY

 

690.512

3.565

*

DAY(1)

0.289

 

p = 0.47

 

DAY(2)

0.299

   

DAY(3)

0.275

   

DAY(4)

0.727

   

Headache

0.577

688.237

5.840

 

Dizziness and fainting (dizz/fain)

0.350

692.043

2.033

 

Weakness lower limb (wllimb)

0.679

684.180

9.897

 

Arthralgia

0.836

685.191

8.886

 

Myalgia

0.865

683.291

10.785

 

Body ache

0.591

685.800

8.277

 

Nausea

−0.108

693.888

0.189

*

Vomit

0.293

692.199

1.878

 

Anorexia

0.332

689.065

5.012

 

Abdominal Epigastic pain (gastric)

0.782

676.656

17.421

 

Petechiea Rash (p.rash)

−0.316

691.154

2.922

 

Flush face (flushf)

0.079

693.962

0.115

*

Bleeding tendency (bt)

0.974

669.752

24.325

 

Chill and rigor (chillnr)

0.630

693.592

0.485

*

Hepatomegaly (hepa)

0.377

691.557

2.520

 

Macular

0.153

692.233

1.844

 

Simple linear Correlation and Correlation Coefficient for continuous variable

Pearson’s correlation value (r) between the significant parameters (r ≥ ±0.8)

Table 6
Full size table

Linearity test

Estimated probability versus independent variables

figure a

Predicted Probability of the Risk versus the Resistance

figure b

Predicted Probability of the Risk versus the Reactance

figure c

Predicted Probability of the Risk versus the Body Cell Mass

figure d

Predicted Probability of the Risk versus the Extracellular Mass

figure e

Predicted Probability of the Risk versus the Body Mass Index

figure f

Predicted Probability of the Risk versus ratio of the Total Body Water and the weight

figure g

Predicted Probability of the Risk versus the ratio of Extracellular Water /Intracellular Water

BMI groups

Group

Category

Frequency

0

Less than 18.5

85

1

18.5–21

90

2

21.1–24.9

148

3

25–29.9

96

4

Equal or more than 30

79

Results from simple logistic test for Categorized BMI

Variables

B

-2 Loglikelihood

G-value compared with the null model

NBMI

 

641.373

52.703

NBMI(1)

0.704

  

NBMI(2)

0.684

  

NBMI(3)

1.887

  

NBMI(4)

1.754

  

Fraction polynomial method chi-square(1df = 3.841, 3df = 7.815)

Summary of the use of the Fractional Polynomial Method for Resistance

No

Variables

df

-2 loglikelihood

G-value for model versus linear

1

RES

1

667.485

 

2

RES,RES3

2

665.278

2.206

3

RES,RES3,RES−2

4

665.278

2.206

Summary of the use of the Fractional Polynomial Method for Reactance

No

Variables

df

-2 loglikelihood

G-value for model versus linear

1

REACT

1

675.589

 

2

REACT,REACT3

2

672.406

3.182

3

REACT,REACT3,REACT−2

4

672.398

3.191

Summary of the use of the Fractional Polynomial Method for Body Cell Mass

No

Variables

df

-2 loglikelihood

G-value for model versus linear

1

BCM

1

674.458

 

2

BCM,BCM3

2

673.492

0.965

3

BCM,BCM3,BCM−2

4

672.717

1.740

Summary of the use of the Fractional Polynomial Method for Extracellular Mass

No

Variables

df

-2 loglikelihood

G-value for model versus linear

1

ECM

1

688.237

 

2

ECM,ECM3

2

686.969

1.268

3

ECM,ECM3,ECM−2

4

686.080

2.157

Summary of the use of the Fractional Polynomial Method for Body Mass Index

No

Variables

df

-2 loglikelihood

G-value for model versus linear

1

BMI

1

647.612

 

2

BMI,BMI3

2

646.654

0.957

3

BMI,BMI3,BMI−2

4

646.601

1.010

Summary of the use of the Fractional Polynomial Method for the ratio of the Total Body Water and the Weight

No

Variables

df

-2 loglikelihood

G-value for model versus linear

1

TRT

1

683.804

 

2

TRT,TRT3

2

682.241

1.562

3

TRT,TRT3,TRT−2

4

681.791

2.013

Summary of the use of the Fractional Polynomial Method for the ratio of the Extracellular water and Intracellular Water (ICW)

No

Variables

df

-2 loglikelihood

G-value for model versus linear

1

ERI

1

671.340

 

2

ERI,ERI3

2

666.254

5.085*

3

ERI,ERI3,ERI−2

4

665.269

6.071

ERI Groups

Group

Category

Frequency

0

0.5–0.65

86

1

0.651–0.75

123

2

0.751–0.85

82

3

0.851–0.95

73

4

Equal or more than 0.951

134

Results from simple logistic test for Categorized ERI

Variables

B

-2 Loglikelihood

G-value compared with the null model

NERI

 

674.267

19.810

NERI(1)

0.100

  

NERI(2)

0.144

  

NERI(3)

0.154

  

NERI(4)

0.996

  

Multiple Logistic regression

Null hypothesis states that removing the variable from the model does not affect the model Summary of the removed variables

Variables

Removed Variables

df

-2 Loglikelihood

G-value compared with the full model

p-value

model 1

TRT, vomit

2

561.468

0.592

>0.05

model 2

hepa,ECM,arthalgia

3

562.076

0.608

>0.05

model 3

headache,macular, backache

2

563.944

1.868

>0.05

model 4

anorexia,sex

2

566.330

2.385

>0.05

model 5

myalgia

1

568.799

2.470

>0.05

model 6

dizzfain

1

570.180

1.381

>0.05

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Faisal, T., Taib, M.N. & Ibrahim, F. Neural network diagnostic system for dengue patients risk classification. J Med Syst 36, 661–676 (2012). https://doi.org/10.1007/s10916-010-9532-x

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