Detection of colorectal adenomas using artificial intelligence models in patients with chronic hepatitis C

Table 1 Patient demographics

Variables	Case (n = 109)	Control (n = 97)	OR	95%CI	P value
Age (mean ± SD in yr)	62.73 ± 9.146	60.20 ± 7.062			0.02a
Age median (yr), n (%)	49 (50.5)	58 (53.2)	0.54	0.31-0.95	0.03a
Gender, n (%) - female	48 (44)	46 (47.4)	0.87	0.50-1.51	0.62
BMI, median (IQR)	28 (7)	32 (7)			0.001a
Family history of hepatitis, n (%)	10 (9.2)	6 (6.2)	1.53	0.55-4.38	0.42
Aspirin use, n (%)	22 (20.2)	35 (36.1)	0.44	0.24-0.83	0.01a
Smoking, n (%)			2.31	1.31-4.07	0.004a
No	51 (46.8)	65 (67)
Yes	38 (34.9)	15 (15.5)
Former	20 (18.3)	17 (17.5)
Total pack years, median (IQR)	25 (21)	25 (11)			0.75
Alcohol use, n (%)	12 (11)	24 (24.7)	0.37	0.17-0.80	0.01a
DM, n (%)	27 (24.8)	31 (32)	0.87	0.50-1.51	0.25a
HIV, n (%)	3 (2.8)	1 (1)	2.71	0.27-26.56	0.62a
Adenomatous polyps present	58 (53.2)	33 (34)	2.20	1.25-3.87	0.006a
Bowel preparation (%)					0.14
Good	90 (82.6)	88 (90.7)
Fair	11(10.1)	7 (7.2)
Poor	8 (7.3)	2 (2.1)

^aP < 0.05

OR: Odds ratio; CI: Confidence interval; BMI: Body mass index; IQR: Interquartile range; DM: Diabetes mellitus; HIV: Human immunodeficiency virus.

Table 2 Machine learning model training

ML model	Test accuracy (%)	Precision (%)	Recall (%)	F1 score (%)
Support vector classifier	52	51	51	51
Random forest	53	53	53	53
Bernoulli naïve Bayes	56	55	55	54
Gradient boosting	50	49	49	48
Logistic regression	50	48	48	47
Deep neural networks	53	53	53	51

ML: Machine learning.

Table 3 Machine learning models and their mean absolute error

ML model	Mean absolute error
Linear regression	1.072
LGBM regressor	1.106
XGBoost regressor	1.273
ElasticNet	0.941
Gradient boosting regressor	1.139
Support vector regressor	0.905

ML: Machine learning.

Table 4 Machine learning models and their performance

ML model	Performance
Support vector classifier	Good
Random forest	Good
Bernoulli naïve Bayes	Optimal
Gradient boosting	Inadequate
Logistic regression	Inadequate
Deep neural networks	Good

ML: Machine learning.