About: Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach
Creator	Guo, Qiyong Pei, Dongmei Quan, Yu Zhang, Chengpu
Source	PMC
abstract	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.
has issue date	2019-01-22(xsd:dateTime)
bibo:doi	10.1155/2019/4248218
bibo:pmid	30805372
has license	cc-by
sha1sum (hex)	0dc958db13e5ac3b76b8204a8081ad0cec38bcfa
schema:url	https://doi.org/10.1155/2019/4248218
resource representing a document's title	Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach
has PubMed Central identifier	PMC6362481
has PubMed identifier	30805372
schema:publication	J Diabetes Res
resource representing a document's body	covid:0dc958db13e5ac3b76b8204a8081ad0cec38bcfa#body_text
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