基于SMOTE和gcForest的医疗小样本数据分类研究

doi:10.11959/j.issn.2096-3750.2023.00337

Abstract

Abstract:

Aiming at the problem of poor classification performance in traditional machine learning models caused by shallow model structure and complex data characteristics in small medical sample data, an combine multi- grained improved cascade forest (cgicForest) model was proposed.It enhances the representation learning ability of the model by adding random sampling into the multi-grained scanning and optimizing the transformation features.It also enhances the model's classification ability by updating the cascade forest’s hierarchical structure.Considering category imbalance problems in datasets, the safe-borderline-SMOTE (SBS) algorithm was proposed to dynamic interpolate around the few class samples belonging to the safety boundary, which can improve the quality of training data.The cgicForest was applied for training and learning, thus the SBS-cgicForest classification model was obtained which can support imbalanced medical small samples data.The model is used on three medical datasets for classification experiments.The results show that the performance indexes of the cgicForest model in the classification of medical small sample data with complex characteristics have increased by 4.1～5.4 percentage points, compared with the multi-grained cascade forest (gcForest) model.The performance indexes have increase by 6.6～11.2 percentage points after the combination with SBS algorithm, the F₁ score was 2～2.5 percentage points higher than that obtained by traditional sampling methods.It provides a reference for solving the classification problem of small medical sample data, and includes support for internet of things applications in smart medical scenarios.

Key words: medical data, small sample, SMOTE, gcForest

CLC Number:

TP391

Wenchang LIU, Yun WEI, Haoxuan YUAN, Yue GAO. Research on medical small sample data classification based on SMOTE and gcForest[J]. Chinese Journal on Internet of Things, 2023, 7(2): 76-87.

Figures/Tables 11

References 30

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数据集	实例数	特征数	少数类数量	数据不平衡比例
Diabetes	768	8	268	1.87
ECG	452	176	207	1.18
Cirrhosis	418	17	144	1.90

对比项	实际为正例	实际为负例
预测为正例	TP	FP
预测为负例	FN	TN

数据集	指标	LR	SVM	gcForest	TBDForest	cgicForest
Diabetes	ACC	0.776 0	0.760 4	0.781 2	0.786 5	0.802 1
	PRE	0.759 9	0.729 2	0.756 9	0.756 9	0.779 7
	REC	0.699 6	0.696 5	0.720 3	0.745 3	0.752 6
	F₁	0.714 7	0.707 0	0.732 4	0.750 4	0.763 0
	AUC	0.849 9	0.805 1	0.853 4	0.838 5	0.857 1
ECG	ACC	0.708 0	0.699 1	0.761 1	0.769 9	0.805 3
	PRE	0.708 5	0.698 6	0.761 8	0.771 9	0.815 9
	REC	0.701 1	0.692 9	0.756 0	0.764 2	0.797 0
	F₁	0.702 0	0.693 7	0.757 3	0.765 8	0.799 6
	AUC	0.767 0	0.798 2	0.810 8	0.814 6	0.822 2
Cirrhosis	ACC	0.781 0	0.742 9	0.752 4	0.752 4	0.763 3
	PRE	0.781 5	0.715 7	0.736 9	0.732 2	0.753 9
	REC	0.713 8	0.691 4	0.685 4	0.692 0	0.692 0
	F₁	0.728 9	0.699 2	0.696 7	0.702 5	0.702 5
	AUC	0.807 2	0.755 2	0.759 3	0.756 8	0.788 4

数据集	指标	LR	SVM	gcForest	TBDForest	cgicForest
Diabetes	ACC	0.804 0	0.868 0	0.868 0	0.873 6	0.884 0
	PRE	0.804 1	0.869 0	0.870 7	0.873 7	0.885 8
	REC	0.804 1	0.868 2	0.868 3	0.870 3	0.884 3
	F₁	0.804 0	0.867 9	0.867 8	0.872 9	0.883 9
	AUC	0.876 2	0.927 0	0.933 9	0.934 2	0.938 7
ECG	ACC	0.780 5	0.772 4	0.821 1	0.826 7	0.837 4
	PRE	0.793 4	0.779 3	0.832 5	0.835 6	0.844 9
	REC	0.791 0	0.780 2	0.831 3	0.835 8	0.846 0
	F₁	0.780 4	0.772 3	0.821 1	0.824 9	0.837 4
	AUC	0.840 6	0.869 8	0.902 9	0.904 1	0.9 080
Cirrhosis	ACC	0.715 3	0.766 4	0.832 1	0.835 3	0.846 7
	PRE	0.712 9	0.772 7	0.831 8	0.835 8	0.846 4
	REC	0.718 8	0.772 0	0.834 0	0.835 9	0.848 7
	F₁	0.715 3	0.766 4	0.831 8	0.833 9	0.846 4
	AUC	0.796 2	0.834 4	0.913 0	0.914 7	0.918 1

Research on medical small sample data classification based on SMOTE and gcForest

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