Accurate and rapid screening model for potential diabetes mellitus

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.

A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes

“…The DLM included seven NIVs (age, male gender, hypertension, family history of diabetes, smoking status, BMI, and waist circumference) that would be convenient for a layperson to use as a self-assessment of diabetes risk in the real world. These variables are highly correlated with undiagnosed diabetes in other studies [38][39][40][41][42][43][44][45]. Although blood analysis (including FPG and oral glucose tolerance test) are required to diagnose at-risk individuals based on the guidelines, our model can provide users with an estimation of their diabetes status without a medical diagnosis.…”

“…Thus, the development of disease prediction or screening models should be user-centric. Pei et al developed a diabetes prediction model using non-invasive variables based on machine learning algorithms (decision tree, AdaBoost, support vector machine, Bayesian network, naïve Bayesian) that showed an appropriate performance [38].…”

A multi-class classification model for supporting the diagnosis of type II diabetes mellitus

“…Several types of machine-learning algorithms, including instance-based ( Esteban et al, 2017 ; Kagawa et al, 2017 ; Nilashi et al, 2017 ; Pei et al, 2019 ; Talaei-Khoei & Wilson, 2018 ), decision trees ( Alghamdi et al, 2017 ; Esteban et al, 2017 ; Pei et al, 2019 ; Talaei-Khoei & Wilson, 2018 ), artificial neural network ( Esteban et al, 2017 ; Nilashi et al, 2017 ; Talaei-Khoei & Wilson, 2018 ), ensemble ( Alghamdi et al, 2017 ; Esteban et al, 2017 ; Pei et al, 2019 ), Bayesian ( Alghamdi et al, 2017 ; Anderson et al, 2015 ; Esteban et al, 2017 ; Maniruzzaman et al, 2017 ; Pei et al, 2019 ), statistical model ( Alghamdi et al, 2017 ; Esteban et al, 2017 ; Maniruzzaman et al, 2017 ; Talaei-Khoei & Wilson, 2018 ; Wu et al, 2018 ), and others (see Table 1 ), have been adopted to predict T2DM-related issues. However, these studies revealed different results in predicting the onset of T2DM even with the same machine-learning algorithm.…”

“…For example, Pei et al (2019) and Alghamdi et al (2017) both adopted J48 as one of their algorithms for predicting the onset of T2DM, only Pei et al (2019) found that J48 had the best performance. The performance of support vector machine also differs among opposing studies ( Esteban et al, 2017 ; Kagawa et al, 2017 ; Nilashi et al, 2017 ; Pei et al, 2019 ; Talaei-Khoei & Wilson, 2018 ). Further, not all inclusionary studies adopted the same algorithms, making it difficult to accurately compare the performance of differing algorithms.…”

“…Numerous studies have utilized machine-learning techniques to predict the onset of T2DM. While previous DM prediction studies have shown a potential for detecting the onset of T2DM ( Alghamdi et al, 2017 ; Anderson et al, 2015 ; Esteban et al, 2017 ; Kagawa et al, 2017 ; Maniruzzaman et al, 2017 ; Nilashi et al, 2017 ; Pei et al, 2019 ; Talaei-Khoei & Wilson, 2018 ; Upadhyaya et al, 2017 ; Wu et al, 2018 ), no studies, to our knowledge, have been aimed at predicting a suitable diagnosis code for T2DM patients. Further, ensemble machine-learning techniques such as bagging and boosting approaches are less utilized in these studies ( Esteban et al, 2017 ).…”