Temporal Data and Diabetes Classification in Thailand
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Abstract
- Diabetes mellitus is a chronic disease that reduces quality of life since it often causes other complications such as heart disease, high blood pressure, neuropathy and the loss of some organs in the body. This work proposes a temporal features extraction model which extracts the features embedded in historical data such as health examination data for classification. The proposed model can be used with any promising classification methods such as Naïve Bayes, Logistic Regression, C4.5 (J48), Bagging and SVMs. This work evaluates the proposed method on health examination data during 2004-2010 (7 years) of factory employees in Thailand. It consists of 43,523 employees in total where 28,808 employees have only one record and 14,715 employees are examined more than once. Resampling with replacement is applied to the dataset for balancing training instances among the classes before proceeding to training process. Features used for diabetes classification are categorized into three groups: Physical Examination, Urinalysis and Biochemistry. The results of experiments show that the data with temporal feature gains higher classification performance than the data without temporal feature.
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