Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach
Background. Diabetes mellitus is a chronic disease with a steadfast increase in prevalence. Due to the chronic course of the disease combining with devastating complications, this disorder could easily carry a financial burden. The early diagnosis of diabetes remains as one of the major challenges m...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Diabetes Research |
| Online Access: | http://dx.doi.org/10.1155/2019/4248218 |
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| author | Dongmei Pei Chengpu Zhang Yu Quan Qiyong Guo |
| author_facet | Dongmei Pei Chengpu Zhang Yu Quan Qiyong Guo |
| author_sort | Dongmei Pei |
| collection | DOAJ |
| description | Background. Diabetes mellitus is a chronic disease with a steadfast increase in prevalence. Due to the chronic course of the disease combining with devastating complications, this disorder could easily carry a financial burden. The early diagnosis of diabetes remains as one of the major challenges medical providers are facing, and the satisfactory screening tools or methods are still required, especially a population- or community-based tool. Methods. This is a retrospective cross-sectional study involving 15,323 subjects who underwent the annual check-up in the Department of Family Medicine of Shengjing Hospital of China Medical University from January 2017 to June 2017. With a strict data filtration, 10,436 records from the eligible participants were utilized to develop a prediction model using the J48 decision tree algorithm. Nine variables, including age, gender, body mass index (BMI), hypertension, history of cardiovascular disease or stroke, family history of diabetes, physical activity, work-related stress, and salty food preference, were considered. Results. The accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC) value for identifying potential diabetes were 94.2%, 94.0%, 94.2%, and 94.8%, respectively. The structure of the decision tree shows that age is the most significant feature. The decision tree demonstrated that among those participants with age≤49, 5497 participants (97%) of the individuals were identified as nondiabetic, while age>49, 771 participants (50%) of the individuals were identified as nondiabetic. In the subgroup where people were 34<age≤49 and BMI≥25, when with positive family history of diabetes, 89 (92%) out of 97 individuals were identified as diabetic and, when without family history of diabetes, 576 (58%) of the individuals were identified as nondiabetic. Work-related stress was identified as being associated with diabetes. In individuals with 34<age≤49 and BMI≥25 and without family history of diabetes, 22 (51%) of the individuals with high work-related stress were identified as nondiabetic while 349 (88%) of the individuals with low or moderate work-related stress were identified as not having diabetes. Conclusions. We proposed a classifier based on a decision tree which used nine features of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of diabetes. The classifier indicates that a decision tree analysis can be successfully applied to screen diabetes, which will support clinical practitioners for rapid diabetes identification. The model provides a means to target the prevention of diabetes which could reduce the burden on the health system through effective case management. |
| format | Article |
| id | doaj-art-eda0d85cda514307bf6bc82987fa52a8 |
| institution | Kabale University |
| issn | 2314-6745 2314-6753 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Research |
| spelling | doaj-art-eda0d85cda514307bf6bc82987fa52a82025-02-03T06:00:54ZengWileyJournal of Diabetes Research2314-67452314-67532019-01-01201910.1155/2019/42482184248218Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree ApproachDongmei Pei0Chengpu Zhang1Yu Quan2Qiyong Guo3Department of Family Medicine, Shengjing Hospital, China Medical University, Shenyang, Liaoning, ChinaDepartment of Family Medicine, Shengjing Hospital, China Medical University, Shenyang, Liaoning, ChinaDepartment of Informatics, Shengjing Hospital, China Medical University, Shenyang, Liaoning, ChinaDepartment of Radiology, Shengjing Hospital, China Medical University, Shenyang, Liaoning, ChinaBackground. Diabetes mellitus is a chronic disease with a steadfast increase in prevalence. Due to the chronic course of the disease combining with devastating complications, this disorder could easily carry a financial burden. The early diagnosis of diabetes remains as one of the major challenges medical providers are facing, and the satisfactory screening tools or methods are still required, especially a population- or community-based tool. Methods. This is a retrospective cross-sectional study involving 15,323 subjects who underwent the annual check-up in the Department of Family Medicine of Shengjing Hospital of China Medical University from January 2017 to June 2017. With a strict data filtration, 10,436 records from the eligible participants were utilized to develop a prediction model using the J48 decision tree algorithm. Nine variables, including age, gender, body mass index (BMI), hypertension, history of cardiovascular disease or stroke, family history of diabetes, physical activity, work-related stress, and salty food preference, were considered. Results. The accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC) value for identifying potential diabetes were 94.2%, 94.0%, 94.2%, and 94.8%, respectively. The structure of the decision tree shows that age is the most significant feature. The decision tree demonstrated that among those participants with age≤49, 5497 participants (97%) of the individuals were identified as nondiabetic, while age>49, 771 participants (50%) of the individuals were identified as nondiabetic. In the subgroup where people were 34<age≤49 and BMI≥25, when with positive family history of diabetes, 89 (92%) out of 97 individuals were identified as diabetic and, when without family history of diabetes, 576 (58%) of the individuals were identified as nondiabetic. Work-related stress was identified as being associated with diabetes. In individuals with 34<age≤49 and BMI≥25 and without family history of diabetes, 22 (51%) of the individuals with high work-related stress were identified as nondiabetic while 349 (88%) of the individuals with low or moderate work-related stress were identified as not having diabetes. Conclusions. We proposed a classifier based on a decision tree which used nine features of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of diabetes. The classifier indicates that a decision tree analysis can be successfully applied to screen diabetes, which will support clinical practitioners for rapid diabetes identification. The model provides a means to target the prevention of diabetes which could reduce the burden on the health system through effective case management.http://dx.doi.org/10.1155/2019/4248218 |
| spellingShingle | Dongmei Pei Chengpu Zhang Yu Quan Qiyong Guo Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach Journal of Diabetes Research |
| title | Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach |
| title_full | Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach |
| title_fullStr | Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach |
| title_full_unstemmed | Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach |
| title_short | Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach |
| title_sort | identification of potential type ii diabetes in a chinese population with a sensitive decision tree approach |
| url | http://dx.doi.org/10.1155/2019/4248218 |
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