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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (2): 116-128    DOI: 10.11925/infotech.2096-3467.2020.0353
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Predicting Diabetic Complications with Unbalanced Data
Qiu Yunfei,Guo Lei()
School of Software, Liaoning Technical University, Huludao 125105, China
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Abstract  

[Objective] This paper addresses the classification issues facing unbalanced sample data, aiming to find a better solution and improve the prediction results of diabetic complications. [Methods] At the data level, we used the improved SMOTE oversampling algorithm (F_SMOTE) to change the class distribution of unbalanced data. At the algorithm level, we adopted the balance accuracy, ROC and AUC under PR curve as evaluation criteria. Finally, we compared the performance of four single classifier learning models and four ensemble learning models. [Results] Compared with the traditional over sampling algorithm, our F_SMOTE algorithm improved the prediction accuracy, ROC and PR by 1.49%, 3.43% and 8.05%, respectively. Compared with the single classifier learning model, our method improved the accuracy, ROC and PR by 9.73%, 14.07% and 46.79%, respectively. The combined F_SMOTE algorithm and Random Forest model reached 97.64% in accuracy, 98.91% in ROC and 96.64% in PR for unbalanced data. [Limitations] The coverage and efficiency of our model training needs to be further improved. [Conclusions] This method creates a predictive analysis framework for researchers, which could also help doctors in disease diagnosis and prevention.
Key words： Unbalanced Data      F_SMOTE Algorithm      Ensemble Learning      Diabetic Complications     
Received: 08 June 2020      Published: 11 November 2020
ZTFLH:  G350  
Corresponding Authors: Guo Lei ORCID：0000-0003-3441-5063     E-mail: 752714018@qq.com
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Yunfei Qiu
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Cite this article:

Qiu Yunfei, Guo Lei. Predicting Diabetic Complications with Unbalanced Data. Data Analysis and Knowledge Discovery, 2021, 5(2): 116-128.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0353     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I2/116

样本分布 煤矿高能地震波数据集 肺癌患者
数据集		 心力衰竭
患者数据集
正样本数量（例） 170 70 96
负样本数量（例） 2 414 400 203
正负样本比例 1∶14 1∶6 1∶2
Statistics of Unbalanced Distribution of Samples
Result of Evaluation Index
Investigation Results of Each Field of Biochemical Table
并发症所属类型 并发症细分 英文名 本文命名
慢性并发症 糖尿病肾病 Diabetic Nephropathy D1
糖尿病视网膜病变 Diabetic Retinopathy D2
糖尿病神经病变 Diabetic Neuropathy D3
糖尿病心血管病变 Diabetic Cardiovascular Disease D4
糖尿病脑血管病变 Diabetic Cerebrovascular Disease D5
糖尿病下肢血管病变 Diabetic Lower Extremity Angiopathy D6
糖尿病病足 Diabetic Foot D7
急性并发症 糖尿病酮症酸中毒 Diabetic Ketoacidosis D8
糖尿病高渗性高血糖 Diabetic Hyperosmotic Hyperglycemia D9
糖尿病乳酸性中毒 Diabetic Lactic Acidosis D10
糖尿病严重低血糖 Diabetic Severe Hypoglycemia D11
11 Types of High Complications and Nomenclature
分布情况 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11
正类样本 2 684 421 316 5 999 2 350 542 76 553 39 173 167
负类样本 8 448 10 711 10 816 5 133 8 782 10 590 11 056 10 579 11 093 10 959 10 965
正负样本比例 0.32 0.04 0.03 1.17 0.27 0.05 0.01 0.05 0.01 0.02 0.02
属性个数 27 27 27 27 27 27 27 27 27 27 27
Detailed Description of Each Complication Data
分布情况 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11
样本总量 3 341 3 341 3 340 3 340 3 340 3 340 3 340 3 340 3 340 3 340 3 341
正样本例数 806 127 95 1 540 705 163 23 166 12 52 51
负样本例数 2 535 3 214 3 245 1 800 2 635 3 177 3 317 3 174 3 328 3 288 3 290
正例所占比例 0.24 0.04 0.03 0.46 0.21 0.05 0.01 0.05 0.01 0.02 0.02
Distribution of Positive and Negative Samples of Complications in the Test Set
分布情况 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11
正样本例数 5 913 7 497 7 571 4 199 6 147 7 413 7 739 7 405 7 765 7 671 7 675
负样本例数 5 913 7 497 7 571 4 199 6 147 7 413 7 739 7 405 7 765 7 671 7 675
样本总量 11 826 14 994 15 142 8 398 12 294 14 826 15 478 14 810 15 530 15 342 15 350
The Distribution of Sample Number after Over-Sampling Methods
对比算法 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11
重采样 LR 0.79 0.74 0.59 0.65 0.62 0.61 0.77 0.73 0.90 0.85 0.87
SVM 0.77 0.74 0.60 0.63 0.64 0.64 0.78 0.76 0.91 0.88 0.88
kNN 0.86 0.95 0.96 0.72 0.79 0.94 0.99 0.96 1.00 0.99 0.99
DT 0.94 0.98 0.99 0.80 0.91 0.98 1.00 0.99 1.00 1.00 1.00
RF 0.95 0.99 1.00 0.86 0.95 1.00 1.00 0.99 1.00 1.00 1.00
GBDT 0.88 0.93 0.94 0.77 0.78 0.90 1.00 0.93 1.00 0.99 0.99
XGBoost 0.88 0.92 0.94 0.77 0.77 0.88 0.99 0.93 1.00 0.99 1.00
并行集成 0.88 0.93 0.98 0.78 0.83 0.94 1.00 0.95 1.00 0.99 1.00
SMOTE LR 0.79 0.75 0.62 0.65 0.63 0.61 0.77 0.73 0.92 0.87 0.88
SVM 0.76 0.76 0.62 0.64 0.65 0.65 0.81 0.76 0.92 0.88 0.88
kNN 0.88 0.93 0.94 0.70 0.82 0.92 0.98 0.94 0.99 0.98 0.98
DT 0.89 0.96 0.97 0.70 0.84 0.95 0.99 0.96 1.00 0.99 0.99
RF 0.93 0.98 0.99 0.79 0.91 0.98 1.00 0.99 1.00 1.00 1.00
GBDT 0.90 0.94 0.96 0.76 0.83 0.92 0.99 0.95 1.00 0.99 1.00
XGBoost 0.90 0.94 0.95 0.76 0.82 0.92 0.99 0.94 1.00 0.99 1.00
并行集成 0.89 0.94 0.97 0.76 0.83 0.95 0.99 0.95 1.00 0.98 1.00
F_SMOTE LR 0.80 0.78 0.71 0.67 0.64 0.64 0.80 0.76 0.95 0.87 0.90
SVM 0.80 0.80 0.68 0.60 0.63 0.67 0.85 0.76 0.94 0.90 0.89
kNN 0.90 0.96 0.94 0.72 0.82 0.92 0.99 0.97 0.99 0.99 0.99
DT 0.92 0.98 0.98 0.73 0.85 0.95 0.99 0.98 1.00 1.00 1.00
RF 0.95 0.98 1.00 0.87 0.97 0.98 1.00 0.99 1.00 1.00 1.00
GBDT 0.92 0.95 0.96 0.80 0.84 0.92 1.00 0.97 1.00 0.98 0.99
XGBoost 0.91 0.94 0.96 0.79 0.83 0.91 0.99 0.97 1.00 0.98 0.99
并行集成 0.94 0.98 0.99 0.81 0.90 0.98 1.00 0.98 1.00 0.99 1.00
Classification Accuracy of Three Over-Sampling Algorithms
ROC and PR Curves of Diabetic Nephropathy Treated with F_ SMOTE + RF and SMOTE + RF
对比算法 AUC D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11
重采样 LR ROC 0.83 0.76 0.55 0.71 0.65 0.59 0.70 0.37 0.28 0.69 0.88
PR 0.65 0.11 0.05 0.69 0.37 0.06 0.02 0.04 0.01 0.05 0.38
SVM ROC 0.83 0.68 0.45 0.70 0.62 0.54 0.65 0.45 0.61 0.54 0.85
PR 0.65 0.08 0.03 0.68 0.29 0.05 0.04 0.05 0.01 0.02 0.49
kNN ROC 0.93 0.90 0.91 0.80 0.84 0.91 0.97 0.95 0.99 0.96 0.98
PR 0.82 0.23 0.20 0.83 0.59 0.22 0.15 0.39 0.13 0.15 0.54
DT ROC 0.93 0.96 0.97 0.77 0.90 0.95 0.99 0.96 1.00 0.99 0.99
PR 0.73 0.30 0.32 0.86 0.64 0.30 0.33 0.37 0.26 0.37 0.48
RF ROC 0.98 0.99 1.00 0.97 0.99 1.00 1.00 0.99 1.00 1.00 1.00
PR 0.94 0.80 0.81 0.97 0.95 0.88 0.75 0.79 0.90 0.76 0.92
GBDT ROC 0.95 0.95 0.94 0.86 0.87 0.90 1.00 0.96 1.00 0.98 1.00
PR 0.87 0.44 0.55 0.86 0.65 0.40 0.90 0.61 0.92 0.73 0.94
XGBoost ROC 0.95 0.94 0.95 0.86 0.86 0.90 1.00 0.96 1.00 0.99 1.00
PR 0.87 0.38 0.53 0.86 0.63 0.36 0.84 0.60 0.90 0.71 0.94
并行集成 ROC 0.97 0.98 0.99 0.93 0.98 0.98 1.00 0.98 1.00 0.99 1.00
PR 0.91 0.63 0.67 0.95 0.89 0.71 0.89 0.68 0.90 0.79 0.94
SMOTE LR ROC 0.84 0.75 0.55 0.71 0.67 0.62 0.75 0.68 0.78 0.78 0.90
PR 0.67 0.15 0.04 0.69 0.39 0.07 0.04 0.14 0.02 0.02 0.44
SVM ROC 0.84 0.73 0.53 0.70 0.66 0.62 0.57 0.62 0.75 0.75 0.90
PR 0.64 0.14 0.06 0.68 0.33 0.08 0.01 0.10 0.01 0.01 0.62
kNN ROC 0.92 0.88 0.85 0.80 0.83 0.88 0.95 0.91 0.97 0.97 0.98
PR 0.80 0.14 0.09 0.83 0.52 0.17 0.06 0.24 0.07 0.07 0.57
DT ROC 0.95 0.95 0.96 0.77 0.89 0.93 0.99 0.95 1.00 1.00 1.00
PR 0.80 0.28 0.24 0.87 0.58 0.26 0.21 0.32 0.24 0.24 0.61
RF ROC 0.98 0.99 1.00 0.97 0.99 1.00 1.00 0.99 1.00 1.00 1.00
PR 0.94 0.89 0.97 0.97 0.95 0.97 0.92 0.87 0.86 0.86 0.94
GBDT ROC 0.95 0.96 0.94 0.86 0.86 0.90 0.99 0.96 1.00 1.00 1.00
PR 0.88 0.55 0.59 0.86 0.67 0.42 0.88 0.69 0.78 0.78 0.98
XGBoost ROC 0.95 0.95 0.93 0.86 0.86 0.90 0.98 0.96 1.00 1.00 1.00
PR 0.87 0.46 0.50 0.86 0.65 0.37 0.83 0.68 0.77 0.77 0.96
并行集成 ROC 0.97 0.98 0.98 0.93 0.97 0.98 1.00 0.98 1.00 1.00 1.00
PR 0.91 0.66 0.73 0.95 0.89 0.80 0.89 0.76 0.84 0.84 0.95
F_SMOTE LR ROC 0.87 0.80 0.66 0.71 0.69 0.66 0.88 0.82 0.98 0.89 0.96
PR 0.70 0.15 0.05 0.69 0.40 0.07 0.04 0.28 0.12 0.12 0.47
SVM ROC 0.87 0.82 0.68 0.70 0.71 0.70 0.89 0.82 0.99 0.89 0.97
PR 0.70 0.15 0.06 0.68 0.42 0.09 0.05 0.38 0.17 0.17 0.66
kNN ROC 0.92 0.96 0.97 0.76 0.84 0.95 0.99 0.97 1.00 0.99 1.00
PR 0.78 0.32 0.31 0.77 0.54 0.30 0.31 0.44 0.53 0.363 0.72
DT ROC 0.95 0.96 0.98 0.75 0.90 0.96 1.00 0.97 1.00 0.99 1.00
PR 0.81 0.32 0.35 0.79 0.63 0.35 0.57 0.43 0.68 0.38 0.77
RF ROC 0.98 1.00 1.00 0.92 0.98 1.00 1.00 1.00 1.00 1.00 1.00
PR 0.96 0.99 0.99 0.91 0.96 0.99 0.96 0.99 0.92 0.98 0.98
GBDT ROC 0.95 0.97 0.98 0.86 0.87 0.95 1.00 0.98 1.00 1.00 1.00
PR 0.88 0.65 0.77 0.86 0.69 0.58 0.94 0.72 0.82 0.85 0.98
XGBoost ROC 0.95 0.97 0.97 0.86 0.86 0.94 1.00 0.97 1.00 1.00 1.00
PR 0.88 0.62 0.74 0.86 0.67 0.55 0.94 0.70 0.88 0.83 0.98
并行集成 ROC 0.98 1.00 1.00 0.90 0.96 1.00 1.00 1.00 1.00 1.00 1.00
PR 0.93 0.92 0.96 0.89 0.89 0.96 0.96 0.97 0.92 0.96 0.98
Area AUC of the ROC and PR Curves of Three Over-Sampling Algorithms
对比算法 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11
重采样 LR 0.21 0.21 0.38 0.20 0.22 0.22 0.23 0.23 0.27 0.28 0.21
SVM 49.67 100.63 122.14 51.49 74.74 111.16 104.32 92.97 73.50 81.23 43.34
kNN 0.66 0.94 0.99 0.76 0.63 0.82 1.00 1.01 1.08 0.80 0.87
DT 0.35 0.35 0.26 0.34 0.30 0.33 0.22 0.26 0.07 0.18 0.14
RF 15.32 13.09 14.64 13.31 15.38 15.28 10.06 12.76 5.98 9.55 7.61
GBDT 37.99 44.64 39.83 30.54 36.92 41.66 38.21 40.00 34.34 36.31 35.42
XGBoost 8.70 9.65 9.69 6.86 8.19 9.14 9.69 9.55 9.74 8.99 9.09
并行集成 69.88 41.70 42.83 67.99 85.26 44.09 39.02 42.05 40.55 39.64 71.51
SMOTE LR 0.16 0.18 0.21 0.19 0.27 0.21 0.34 0.31 0.25 0.23 0.24
SVM 37.40 72.18 113.49 47.79 75.50 117.72 104.35 96.86 79.36 76.72 50.39
kNN 0.51 0.98 0.89 0.60 0.80 0.83 1.16 0.87 1.12 0.79 1.00
DT 0.40 0.42 0.62 0.35 0.50 0.79 0.88 0.42 0.23 0.59 0.43
RF 12.53 17.24 21.74 14.98 17.52 24.86 29.42 19.14 15.32 20.36 18.10
GBDT 34.86 58.66 65.00 33.56 46.38 67.64 67.65 63.14 57.93 66.29 67.93
XGBoost 7.73 12.05 13.63 8.00 10.23 12.79 13.79 12.90 14.31 15.05 14.06
并行集成 71.13 61.52 62.53 66.25 104.77 67.80 75.04 68.60 62.39 74.85 109.42
F_SMOTE LR 0.23 0.30 0.29 0.17 0.19 0.23 0.25 0.26 0.25 0.18 0.17
SVM 49.33 68.38 82.09 56.17 76.01 86.54 51.93 53.80 26.37 29.36 9.53
kNN 0.87 1.08 0.76 0.88 0.73 0.83 0.82 0.71 0.58 0.57 0.75
DT 0.34 0.52 0.43 0.37 0.39 0.33 0.21 0.38 0.15 0.24 0.16
RF 13.09 12.80 13.69 14.38 14.01 12.44 10.20 11.51 6.48 9.04 7.05
GBDT 40.00 44.47 48.09 34.15 42.19 44.42 43.51 34.21 32.28 31.96 30.66
XGBoost 10.38 10.71 10.19 8.19 8.85 10.40 10.92 8.82 7.61 8.10 7.31
并行集成 74.83 56.42 49.23 77.90 38.24 37.66 44.36 48.10 39.83 39.55 46.18
The Time (Seconds) Spent by Each Algorithm on Three Over-Sampling Methods
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