Abstract
BackgroundPrediction or early diagnosis of diabetes is crucial for populations with high risk of diabetes.MethodsIn this study, we assessed the ability of five popular classifiers (J48, AdaboostM1, SMO, Bayes Net, and Naïve Bayes) to identify individuals with diabetes based on nine non-invasive and easily obtained clinical features, including age, gender, body mass index (BMI), hypertension, history of cardiovascular disease or stroke, family history of diabetes, physical activity, work stress, and salty food preference. A total of 4205 data entries were obtained from annual physical examination reports for adults in the Shengjing Hospital of China Medical University during January–April 2017. Weka data mining software was used to identify the best algorithm for diabetes classification.ResultsThe results indicate that decision tree classifier J48 has the best performance (accuracy = 0.9503, precision = 0.950, recall = 0.950, F-measure = 0.948, and AUC = 0.964). The decision tree structure shows that age is the most significant feature, followed by family history of diabetes, work stress, BMI, salty food preference, physical activity, hypertension, gender, and history of cardiovascular disease or stroke.ConclusionsOur study shows that decision tree analyses can be applied to screen individuals for early diabetes risk without the need for invasive tests. This procedure will be particularly useful in developing regions with high epidemiological risk and poor socioeconomic status, and enable clinical practitioners to rapidly screen patients for increased risk of diabetes. The key features in the tree structure could further facilitate diabetes prevention through targeted community interventions, which can potentially improve early diabetes diagnosis and reduce burdens on the healthcare system.
Highlights
Prediction or early diagnosis of diabetes is crucial for populations with high risk of diabetes
Records with past history of diabetes (291 records) were excluded because we focused on predicting prediabetes and diabetes
The decision tree shows that age was assigned by as the first and most informative node, followed by family history of diabetes, work stress, body mass index (BMI), salty food preference, physical activity, hypertension, gender, and history of cardiovascular disease or stroke
Summary
Prediction or early diagnosis of diabetes is crucial for populations with high risk of diabetes. The worldwide incidence of diabetes rose from 108 million in 1980 to 422 million in 2014, and could potentially be the seventh-leading cause of death in 2030 [1]. The incidence of diabetes (100 million adult patients) in China was the highest worldwide in 2015, whereas 52.7% of these patients (50 million) are undiagnosed [2, 3]. Early detection and prevention of diabetes is a severe challenge in China. China’s National Plan for Non-Communicable Diseases Prevention and Treatment (2012–2015) identified diabetes as one of the priority diseases in China, and proposed several recommendations to predict diabetes based on blood glucose tests and routine physical examinations [5]
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