A Comparative Study on the Prediction of Occupational Diseases in China with Hybrid Algorithm Combing Models

Table 4

Prediction accuracy of hybrid models.

Models	Parameter	Group	ME	RMSE	MAE	MPE	MAPE
GM-KNN	k = 2	Training	240.70	1197.26	556.50	0.74	2.32
		Testing	5634.33	9151.44	6487.00	20.17	23.57
	kernel = inv	Training	0.00	0.00	0.00	0.00	0.00
		Testing	6305.41	9155.53	7479.90	22.21	26.89
GM-SVM	kernel = linear	Training	1055.45	3388.72	2422.38	1.71	11.25
		Testing	−7738.91	8587.02	7738.91	−29.16	29.16
	kernel = polynomial	Training	731.33	2742.63	1970.80	1.33	8.94
		Testing	280.26	1573.30	1280.50	0.78	4.45
	kernel = radial	Training	−11.44	863.23	805.92	−1.04	4.43
		Testing	3964.53	4693.51	3964.53	14.10	14.10
	kernel = sigmoid	Training	1333.48	5934.06	3859.30	4.08	17.64
		Testing	−2810.06	3422.28	2810.06	−10.79	10.79
GM-RF	mtry = 1	Training	212.67	1317.38	1174.73	−0.45	6.02
	ntree = 30	Testing	−804.74	2090.13	1862.25	−3.44	6.99
GM-GBM	nrounds = 100	Training	5.27	418.30	365.87	−0.23	1.86
	colsample_bytree = 1
	min_child_weight = 1
	eta = 0.1	Testing	−1833.39	2661.27	2205.13	−7.21	8.45
	max_depth = 3
	Subsample = 0.5
	Gamma = 0.5
GM–ANN	Size = 5	Training	−3.29	16.29	12.03	−0.01	0.07
	decay = 1e − 08	Testing	−222.60	1076.60	914.97	−1.04	3.49

Note. ME: mean error; MAE: mean absolute error; MPE: mean percentage error; MAPE: mean absolute percentage error.