Data Set Information:

糖尿病患者记录来自两个来源：自动电子记录设备和纸质记录。自动装置有一个内部时钟来标记事件，而纸质记录只提供“逻辑时间”时段（早餐、午餐、晚餐、就寝时间）。对于纸质记录，早餐（08:00）、午餐（12:00）、晚餐（18:00）和就寝时间（22:00）被指定为固定时间。因此，纸质记录具有虚拟的统一记录时间，而电子记录具有更真实的时间戳。

糖尿病文件由每个记录的四个字段组成。每个字段由选项卡分隔，每个记录由换行符分隔。
文件名和格式：
（1） 年月日格式的日期
（2） XX:YY格式的时间
（3） 代码
（4） 价值观

代码字段的破译如下：
33=常规胰岛素剂量
34=NPH胰岛素剂量
35=UltraLente胰岛素剂量
48=未指定的血糖测量值
57=未指定的血糖测量值
58=早餐前血糖测量
59=早餐后血糖测量
60=午餐前血糖测量
61=午餐后血糖测量
62=晚餐前血糖测量
63=晚餐后血糖测量
64=零食前血糖测量
65=低血糖症状
66=典型的膳食摄入
67=超过正常膳食摄入量
68=低于正常膳食摄入量
69=典型的锻炼活动
70=比通常的锻炼活动多
71=少于通常的锻炼活动
72=未指定的特殊事件

Attribute Information:

Diabetes files consist of four fields per record.  Each field is separated by a tab and each record is separated by a newline.

File Names and format:
(1) Date in MM-DD-YYYY format
(2) Time in XX:YY format
(3) Code
(4) Value

Relevant Papers:

N/A

Papers That Cite This Data Set¹:

Jeroen Eggermont and Joost N. Kok and Walter A. Kosters. Genetic Programming for data classification: partitioning the search space. SAC. 2004.  [View Context].

Zhi-Hua Zhou and Yuan Jiang. NeC4.5: Neural Ensemble based C4.5. IEEE Trans. Knowl. Data Eng, 16. 2004.  [View Context].

Prem Melville and Raymond J. Mooney. Diverse ensembles for active learning. ICML. 2004.  [View Context].

Zhihua Zhang and James T. Kwok and Dit-Yan Yeung. Parametric Distance Metric Learning with Label Information. IJCAI. 2003.  [View Context].

Michael L. Raymer and Travis E. Doom and Leslie A. Kuhn and William F. Punch. Knowledge discovery in medical and biological datasets using a hybrid Bayes classifier/evolutionary algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 33. 2003.  [View Context].

Eibe Frank and Mark Hall. Visualizing Class Probability Estimators. PKDD. 2003.  [View Context].

Krzysztof Krawiec. Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks. Institute of Computing Science, Poznan University of Technology. 2002.  [View Context].

Ilya Blayvas and Ron Kimmel. Multiresolution Approximation for Classification. CS Dept. Technion. 2002.  [View Context].

Peter Sykacek and Stephen J. Roberts. Adaptive Classification by Variational Kalman Filtering. NIPS. 2002.  [View Context].

Kristin P. Bennett and Ayhan Demiriz and Richard Maclin. Exploiting unlabeled data in ensemble methods. KDD. 2002.  [View Context].

Marina Skurichina and Ludmila Kuncheva and Robert P W Duin. Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy. Multiple Classifier Systems. 2002.  [View Context].

Robert Burbidge and Matthew Trotter and Bernard F. Buxton and Sean B. Holden. STAR - Sparsity through Automated Rejection. IWANN (1). 2001.  [View Context].

Jochen Garcke and Michael Griebel and Michael Thess. Data Mining with Sparse Grids. Computing, 67. 2001.  [View Context].

Peter L. Hammer and Alexander Kogan and Bruno Simeone and Sandor Szedm'ak. R u t c o r Research R e p o r t. Rutgers Center for Operations Research Rutgers University. 2001.  [View Context].

Marina Skurichina and Robert P W Duin. Boosting in Linear Discriminant Analysis. Multiple Classifier Systems. 2000.  [View Context].

Chris Drummond and Robert C. Holte. Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria. ICML. 2000.  [View Context].

Mark A. Hall. Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning. ICML. 2000.  [View Context].

Endre Boros and Peter Hammer and Toshihide Ibaraki and Alexander Kogan and Eddy Mayoraz and Ilya B. Muchnik. An Implementation of Logical Analysis of Data. IEEE Trans. Knowl. Data Eng, 12. 2000.  [View Context].

Simon Tong and Daphne Koller. Restricted Bayes Optimal Classifiers. AAAI/IAAI. 2000.