Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective cohort study was conducted using the health screening data of adults older than 65 years in Wuhan, China from 2018 to 2020. With a strict data filtration, 127,031 records from the eligible participants were utilized. Overall, 8298 participants were diagnosed with incident T2DM during the 2-year follow-up (2019–2020). The dataset was randomly split into training set (n = 101,625) and test set (n = 25,406). We developed prediction models based on four ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Using LASSO regression, 21 prediction features were selected. The Random under-sampling (RUS) was applied to address the class imbalance, and the Shapley Additive Explanations (SHAP) was used to calculate and visualize feature importance. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The XGBoost model achieved the best performance (AUC = 0.7805, sensitivity = 0.6452, specificity = 0.7577, accuracy = 0.7503). Fasting plasma glucose (FPG), education, exercise, gender, and waist circumference (WC) were the top five important predictors. This study showed that XGBoost model can be applied to screen individuals at high risk of T2DM in the early phrase, which has the strong potential for intelligent prevention and control of diabetes. The key features could also be useful for developing targeted diabetes prevention interventions.

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Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

The technical improvements in healthcare sector today have given rise to many new inventions in the field of artificial intelligence. Patterns for disease identification are carried out, and the onset of prediction of many diseases is detected. Diseases include diabetes mellitus disease, fatal heart diseases, and symptomatic cancer. There are many algorithms that have played a critical role in the prediction of diseases. This paper proposes an ML based approach for diabetes mellitus disease prediction. For diabetes prediction, many ML algorithms are compared and used in the proposed work, and finally the three ML classifiers providing the highest accuracy are determined: RF, GBM, and LGBM. The accuracy of prediction is obtained using two types of datasets. They are Pima Indians dataset and a curated dataset. The ML classifiers LGBM, GB, and RF are used to build a predictive model, and the accuracy of each classifier is noted and compared. In addition to the generalized prediction mechanism, the data augmentation technique is also used, and the final accuracy of prediction is obtained for the classifiers LGBM, GB, and RF. A comparative study and demonstration between augmentation and non-augmentation are also discussed for the two datasets used in order to further improve the performance accuracy for predicting diabetes disease. 

Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation

The technical improvements in healthcare sector today have given rise to many new inventions in the field of artificial intelligence. Patterns for disease identification are carried out, and the onset of prediction of many diseases is detected. Diseases include diabetes mellitus disease, fatal heart diseases, and symptomatic cancer. There are many algorithms that have played a critical role in the prediction of diseases. This paper proposes an ML based approach for diabetes mellitus disease prediction. For diabetes prediction, many ML algorithms are compared and used in the proposed work, and finally the three ML classifiers providing the highest accuracy are determined: RF, GBM, and LGBM. The accuracy of prediction is obtained using two types of datasets. They are Pima Indians dataset and a curated dataset. The ML classifiers LGBM, GB, and RF are used to build a predictive model, and the accuracy of each classifier is noted and compared. In addition to the generalized prediction mechanism, the data augmentation technique is also used, and the final accuracy of prediction is obtained for the classifiers LGBM, GB, and RF. A comparative study and demonstration between augmentation and non-augmentation are also discussed for the two datasets used in order to further improve the performance accuracy for predicting diabetes disease.

Uno de los problemas más comunes en los niños que no son correctamente alimentados es la anemia. La deficiencia de hierro es perjudicial para los menores, pues impide que realicen sus actividades diarias por el cansancio extremo y fatiga. Debido a esta situación, el Estado peruano ha intentado disminuir el nivel de prevalencia de anemia a nivel nacional con campañas médicas en diferentes regiones, pese a ello, localidades como Caylloma en Arequipa aún mantienen un alto porcentaje de infantes anémicos, para ello se desarrolló una implementación mediante Árboles de Decisión con el lenguaje Python para poder determinar si un niño tiene anemia en base a los datos proporcionados.

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Aplicación de los árboles de decisión en el diagnóstico de Anemia en niños de la ciudad de Arequipa

Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review

Background To predict and make an early diagnosis of diabetes is a critical approach in a population with high risk of diabetes, one of the devastating diseases globally. Traditional and conventional blood tests are recommended for screening the suspected patients; however, applying these tests could have health side effects and expensive cost. The goal of this study was to establish a simple and reliable predictive model based on the risk factors associated with diabetes using a decision tree algorithm. Methods A retrospective cross-sectional study was used in this study. A total of 10,436 participants who had a health check-up from January 2017 to July 2017 were recruited. With appropriate data mining approaches, 3454 participants remained in the final dataset for further analysis. Seventy percent of these participants (2420 cases) were then randomly allocated to either the training dataset for the construction of the decision tree or the testing dataset (30%, 1034 cases) for evaluation of the performance of the decision tree. For this purpose, the cost-sensitive J48 algorithm was used to develop the decision tree model. Results Utilizing all the key features of the dataset consisting of 14 input variables and two output variables, the constructed decision tree model identified several key factors that are closely linked to the development of diabetes and are also modifiable. Furthermore, our model achieved an accuracy of classification of 90.3% with a precision of 89.7% and a recall of 90.3%. Conclusion By applying simple and cost-effective classification rules, our decision tree model estimates the development of diabetes in a high-risk adult Chinese population with strong potential for implementation of diabetes management.

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Estimation of Diabetes in a High-Risk Adult Chinese Population Using J48 Decision Tree Model

Background Nonalcoholic fatty liver disease (NAFLD) is the commonest form of chronic liver disease worldwide and its prevalence is rapidly increasing. Screening and early diagnosis of high-risk groups are important for the prevention and treatment of NAFLD; however, traditional imaging examinations are expensive and difficult to perform on a large scale. This study aimed to develop a simple and reliable predictive model based on the risk factors for NAFLD using a decision tree algorithm for the diagnosis of NAFLD and reduction of healthcare costs. Methods This retrospective cross-sectional study included 22,819 participants who underwent annual health examinations between January 2019 and December 2019 at Physical Examination Center in Shengjing Hospital of China Medical University. After rigorous data screening, data of 9190 participants were retained in the final dataset for use in the J48 decision tree algorithm for the construction of predictive models. Approximately 66% of these patients (n=6065) were randomly assigned to the training dataset for the construction of the decision tree, while 34% of the patients (n=3125) were assigned to the test dataset to evaluate the performance of the decision tree. Results The results showed that the J48 decision tree classifier exhibited good performance (accuracy=0.830, precision=0.837, recall=0.830, F-measure=0.830, and area under the curve=0.905). The decision tree structure revealed waist circumference as the most significant attribute, followed by triglyceride levels, systolic blood pressure, sex, age, and total cholesterol level. Conclusion Our study suggests that a decision tree analysis can be used to screen high-risk individuals for NAFLD. The key attributes in the tree structure can further contribute to the prevention of NAFLD by suggesting implementable targeted community interventions, which can help improve the outcome of NAFLD and reduce the burden on the healthcare system.

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Dongmei Pei

Papers

Estimation of Diabetes in a High-Risk Adult Chinese Population Using J48 Decision Tree Model

Estimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach.