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eISSN: 2373-6372

Gastroenterology & Hepatology: Open Access

Editorial Volume 13 Issue 1

Different statistical approaches in liver diseases from conventional vision to disease prediction

Ahed J Alkhatib1,2

1Department of Legal Medicine, Jordan University of Science & Technology, Jordan
2Department of Sociology, International Mariinskaya Academy, Jordan

Correspondence: Ahed J Alkhatib, Department of Legal Medicine, Toxicology and Forensic Medicine, Jordan University of Science & Technology, Jordan, Tel 00962795905145

Received: January 03, 2022 | Published: January 31, 2022

Citation: Alkhatib AJ. Different statistical approaches in liver diseases from conventional vision to disease prediction. Gastroenterol Hepatol Open Access. 2022;13(1):22–24. DOI: 10.15406/ghoa.2022.13.00489

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Editorial

In this editorial, we compared conventional statistical analysis of liver disease data with neural network analysis to predict which risk factors are more important in the prediction of liver diseases. Usually, liver function tests are used to assess liver diseases. These risk factors include, but not restricted to, ALT, AST, total bilirubin and direct bilirubin, total proteins, alkaline phosphatase, and albumin. Some demographical characteristics are also included such as age and gender.

Conventional statistical analysis modes include descriptive analysis such as frequency, percentages, mean, and standard deviations. The relationships between study variables can be assessed using the difference in means such as T test, and One Way ANOVA. Correlations and regressions can also be used to give in depth sights of the liver diseases. The output of these analyses is well known.

With the development of science, machine learning or artificial intelligence has been created to simulate the way of brain activities in thinking. Much progress has been made such as the prediction mode. In this mode of analysis, datasets for various diseases have been created including diabetes, heart, and liver. In general, datasets are designed to have output of two classifications as either diseased or not diseased. The datasets have the data of risk factors, or predictors of liver disease. The artificial intelligence part of this data implies thinking as human brain does. There are three layers in the artificial intelligence, input layer (predictors), hidden layers, and output layer. The system will run many times to give a prediction of which predictors are most important. In the following example, a comparison between conventional and artificial network analysis was made for the same liver disease dataset. Table 1 showed the general characteristics of study variables including frequency, percentage for categorized variables, and mean and standard deviation for non-categorized variables. Table 2 showed the relationships between study variables using independent T test. This test can provide important information including mean, standard deviation, and significance. When significance is equal, it is difficult to determine the relative importance of each predictor.

Variable

Description

Age (M±SD) years

44.75 (16.19)

Gender (N, %):

-                      Males

-                      Females

 

441 (75.6%)

142 (24.4%)

Total bilirubin (M±SD) mg/dl

3.30±6.21

Direct bilirubin (M±SD) mg/dl

1.49±2.81

Alkaline phosphatase (M±SD) IU/L

290.58±242.4

ALT (M±SD) IU/L

80.71±182.62

AST (M±SD) IU/L

109.91±288.92

Total protein (M±SD) g/dl

6.48±1.09

Albumin (M±SD) g/dl

3.14±0.80

Albumin/globulin (%)

0.974±0.32

Health status (N, %):

-                      Diseased

-                      Normal

 

416 (71.4%)

167 (28.6%)

Table 1 General description of study variables

Variable                        

Dataset

N

Mean

Std. Deviation

P value

Age

Disease

416

46.15

15.65

0.001

Normal

167

41.24

16.99

Total bilirubin

Disease

416

4.16

7.14

0.000

 

Normal

167

1.142

1.00

Direct bilirubin

Disease

416

1.92

3.20

0.000

 

Normal

167

0.39

0.52

Alkaline phosphatase

Disease

416

319.00

268.30

0.000

 

Normal

167

219.75

140.98

ALT

Disease

416

99.60

212.76

0.000

 

Normal

167

33.65

25.06

AST

Disease

416

137.69

337.38

0.000

 

Normal

167

40.68

36.41

Total protein

Disease

416

6.45

1.09

0.399

Normal

167

6.54

1.06

Albumin

Disease

416

3.06

0.78

0.000

Normal

167

3.34

0.78

Albumin/globulin_ratio

Disease

414

0.91

0.33

0.000

Normal

165

1.03

0.29

Table 2 The relationships between study variables using independent T test

Using artificial intelligence gives various figures and tables of which Table 3 showed the importance of independent variables. In this table, the importance is given as proportions and percentages. In Figure 1, normalized importance is given according to their importance.

 

Importance

Normalized importance

Age

.063

33.9%

Total bilirubin

.099

53.6%

Direct bilirubin

.121

65.7%

Alkaline phosphatase

.053

28.9%

AST

.169

91.6%

ALT

.185

100.0%

Total protein

.109

58.9%

Albumin

.144

78.1%

Albumin/globulin ratio

.056

30.4%

Table 3 The importance of independent variables

Figure 1 Normalized importance of covariates on liver disease.

Conclusion

Although conventional statistical analysis of liver data can provide important information about liver state conditions, appropriate prediction of health and disease requires neural network analysis.

Acknowledgments

None.

Conflicts of interest

Author declares there are conflicts of interest.

Funding

None.

Creative Commons Attribution License

©2022 Alkhatib. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.

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