Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes

Table 5 The variable importance and rank of the importance of the risk factors derived from machine learning methods

Variables	RF	XGBoost	NB	Average	Rank
Sex	100.0 ± 0	100.0 ± 0	100.0 ± 0	100.0	1.0
Body mass index	54.2 ± 6.6	61.1 ± 14.7	86.2 ± 6.8	67.1	2.0
Age	13.1 ± 7.6	78.3 ± 13.2	67.9 ± 6.5	53.1	3.0
Low density lipoprotein cholesterol	30.4 ± 3.1	8.4 ± 12.8	71.0 ± 7.8	36.6	4.0
Glycated hemoglobin	15.4 ± 5.9	12.8 ± 11.9	48.0 ± 8.3	25.4	5.0
Smoking	12.2 ± 2.7	28.8 ± 9.2	34.5 ± 6.6	25.2	6.0
Creatinine	10.1 ± 2.3	5.3 ± 9.12	53.1 ± 7.3	22.8	7.0
Duration	6.3 ± 4.61	41.5 ± 8.6	10.1 ± 8.9	19.3	8.0
Hemoglobin	8.0 ± 4.16	16.6 ± 8.9	17.0 ± 5.7	13.8	9.0
Blood urine nitrogen	9.0 ± 8.15	6.5 ± 6.79	17.3 ± 9.6	11.0	10.0
Systolic blood pressure	4.2 ± 1.03	21.6 ± 5.1	6.4 ± 2.88	10.7	11.0
Triglyceride	5.4 ± 17.5	15.0 ± 4.4	11.1 ± 12.3	10.5	12.0
Microalbumin	4.3 ± 2.23	3.6 ± 3.83	22.7 ± 6.9	10.2	13.0
Diastolic blood pressure	2.5 ± 5.91	18.9 ± 3.7	5.6 ± 9.33	9.0	14.0
Alainine aminotransferase	3.2 ± 5.96	6.9 ± 3.90	13.0 ± 12.6	7.7	15.0
High density lipoprotein cholesterol	1.3 ± 3.60	9.8 ± 3.29	7.3 ± 8.41	6.1	16.0
HOMA-IR	5.7 ± 2.85	2.2 ± 2.52	10.2 ± 8.1	6.0	17.0
HOMA-B	4.3 ± 2.22	0.0 ± 0.00	7.4 ± 8.831	3.9	18.0

The most important sixth rank. RF: Random forest; XGBoost: eXtreme gradient boosting; NB: Naïve Byes; HOMA-IR: Homeostasis assessment insulin resistance; HOMA-B: homeostasis model assessment of beta-cell function.