Application of Machine Learning Techniques for Clinical Predictive Modeling: A Cross-Sectional Study on Nonalcoholic Fatty Liver Disease in China

Table 4

Accuracy, precision, sensitivity, and F-measure values for the 11 algorithms.

Algorithm	Accuracy	Specificity	Precision	Recall (Sensitivity)	F-measure
kNN	80.26%	0.911	0.620	0.459	0.527
SVM	82.73%	0.946	0.725	0.452	0.557
LR		0.934	0.713	0.518	0.600
NB	81.31%	0.913	0.644	0.496	0.560
BN	82.92%	0.878	0.636	0.675	0.655
C4.5	80.59%	0.892	0.609	0.534	0.569
AdaBoost	81.01%	0.895	0.620	0.542	0.578
Bagging	82.78%	0.910	0.666	0.567	0.613
RF	82.70%	0.932	0.696	0.496	0.579
HNB	82.42%	0.884	0.630	0.649	0.639
AODE	81.07%	0.852	0.592	0.680	0.633
FLI	49.47%	0.812	0.749	0.202	0.318
HIS^#	54.52%	0.544	0.631	0.448	0.524

The equation of FLI was used to predict NAFLD. A FLI < 30 rules out hepatic steatosis and a FLI ≥ 60 confirms fatty liver [10].
The equation of FLI: FLI= () /()100.
^#The equation of FLI was used to predict NAFLD. A HSI of <30.0 rules out NAFLD, while a HSI of >36.0 confirms fatty liver [12].
The equation of HSI: HSI= 8ALT/AST ratio+ BMI (+2 if DM, +2 if female).