Abstract
BackgroundDiabetes Mellitus (DM) has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world. Therefore, it is of great importance to identify individuals at high risk for DM in order to establish prevention strategies for DM.MethodsAiming at the problem of high-dimensional feature space and high feature redundancy of medical data, as well as the problem of data imbalance often faced. This study explored different supervised classifiers, combined with SVM-SMOTE and two feature dimensionality reduction methods (Logistic stepwise regression and LAASO) to classify the diabetes survey sample data with unbalanced categories and complex related factors. Analysis and discussion of the classification results of 4 supervised classifiers based on 4 data processing methods. Five indicators including Accuracy, Precision, Recall, F1-Score and AUC are selected as the key indicators to evaluate the performance of the classification model.ResultsAccording to the result, Random Forest Classifier combining SVM-SMOTE resampling technology and LASSO feature screening method (Accuracy = 0.890, Precision = 0.869, Recall = 0.919, F1-Score = 0.893, AUC = 0.948) proved the best way to tell those at high risk of DM. Besides, the combined algorithm helps enhance the classification performance for prediction of high-risk people of DM. Also, age, region, heart rate, hypertension, hyperlipidemia and BMI are the top six most critical characteristic variables affecting diabetes.ConclusionsThe Random Forest Classifier combining with SVM-SMOTE and LASSO feature reduction method perform best in identifying high-risk people of DM from individuals. And the combined method proposed in the study would be a good tool for early screening of DM.
Highlights
Diabetes Mellitus (DM) has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world
Precision, recall, F1-Score, and Area under the ROC Curve (AUC) were used to Feature selection Given the redundant information that might make the classification results of diabetes unsatisfied in chronic disease survey data, the feature dimension reduction methods, namely Logistic stepwise regression and least absolute shrinkage and selection operator (LASSO), were adopted to retain relevant information and deduct irrelevant information
The classification performance of DM combined with resampling and dimensionality reduction To further improve the classification performance of the model for DM, we further combined the Support vector machine (SVM)-Synthetic minority oversampling technique (SMOTE) resampling technology on the two feature sets extracted by the two feature screening methods to evaluate the classification performance of the four classifiers for DM classification
Summary
Diabetes Mellitus (DM) has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world. It is of great importance to identify individuals at high risk for DM in order to establish prevention strategies for DM. DM has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world [1, 2]. Recent studies have shown that improving lifestyle and medication interventions can prevent diabetic complications, and it may help prevent the onset of type 2 diabetes mellitus (T2DM) [7,8,9,10,11]. It is important to identify individuals at high risk for T2DM and to establish prevention strategies for T2DM
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