Artificial intelligence for hepatitis evaluation

Table 1 Hepatitis detection based on data mining

No.	Task	Algorithms	Sample size (type)	Evaluation index	Ref.
1	Predicting incidence of hepatitis A	ANN; ARIMA	N/A (CDC data)	ANN: Correlation coefficient 0.71; ARIMA: Correlation coefficient 0.66	[12]
2	Predicting incidence of hepatitis B	ARIMA; ElmanNN	486983 cases (data from health commission)	ARIMA: RMSE 0.94, MAE 0.81; ElmanNN: RMSE 0.89, MAE 0.70	[13]
3	Forecasting incidence of hepatitis B	Hybrid method (combing GM and BP-ANN)	10486959 cases (data from health ministry)	R 0.9495, RMSE 4.863 × 103, MAE 3.9704 × 104	[14]
4	Prediction of incidence of hepatitis E	ARIMA; SVM; LSTM	N/A (CDC data)	ARIMA: RMSE 0.022, MAE 0.018; SVM: RMSE 0.0204, MAE 0.0167; LSTM: RMSE 0.01, MAE 0.011	[15]
5	Automated classification of the different stages of hepatitis B	ADHB-ML-MFIS expert system	52 patients (serological data)	Overall accuracy: 0.922; No hepatitis accuracy: 1; Due to infection accuracy: 0.75; Acute HBV accuracy: 0.95; Chronic HBV accuracy: 0.91	[16]
6	Analyzing HBV infection from normal blood samples	Polynomial function; RBF	119 serum samples from HBV infected patients (Raman spectroscopy data)	Polynomial kernel (order-2): Quadratic programming/least squares: Accuracy 98%, precision 97%, sensitivity 100%, specificity 95%; RBF kernel (RBF sigma-2): Quadratic programming: accuracy 94%, precision 90%, sensitivity 100%, specificity 87%; RBF kernel (RBF sigma-2): Least squares: Accuracy 95%, precision 92%, sensitivity 100%, specificity 90%	[8]
7	Rapidly screening hepatitis B from non-hepatitis B	LSTM	1134 blood samples (Raman spectroscopy data)	Accuracy 97.32%, sensitivity 97.87%, specificity 96.77%, precision 96.84%	[17]
8	Finding undiagnosed patients with hepatitis C infection	Logistic regression; Gradient boosting trees; Gradient boosting trees with temporal variables; Stacked ensemble; Random forest	9721923 patients (data from the patient’s medical history)	The stacked ensemble had a specificity of 0.99 and precision of 0.97 at a recall level of 0.50	[19]
9	Predicting hepatitis C virus progression among veterans	CS Cox modellongitudinal Cox model; CS boosting modelLongitudinal-boosting model	72683 CHC individuals (VHA data)	CS Cox model: Concordance 0.746; Longitudinal Cox model: Concordance 0.764; CS boosting model: Concordance 0.758; Longitudinal-boosting model: Concordance 0.774	[20]
10	Forecasting response to IFN plus RIB treatment in HCV patients	ANN	300 patients (serological data)	The diagnostic accuracy rose from 52% (ANN 2) to 70% (ANN 6)	[21]

ANN: Artificial neural network; ARIMA: Autoregressive integrated moving average; CDC: Centers for Disease Control; RMSE: Root-mean-square error; MAE: Mean absolute error; GM: Grey model, BP-ANN: Back propagation artificial neural networks; SVM: Supporter vector machine; LSTM: Long-short time memory neural network; ADHB-ML-MFIS: Automated diagnosis of hepatitis B using multilayer Mamdani fuzzy inference; HBV: Hepatitis B virus; RBF: Gaussian radial basic function; CHC: Chronic hepatitis C virus; VHA: National Veterans Health Administration; IFN: Interferon; RIB: Ribavirin, HCV: Hepatitis C virus.