Prediction of Diabetes Using Data Mining and Machine Learning Algorithms: A Cross-Sectional Study
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Objectives: This study aimed to develop a model to predict fasting blood glucose status using machine learning and datamining, since the early diagnosis and treatment of diabetes can improve outcomes and quality of life. Methods: This crosssectionalstudy analyzed data from 3376 adults over 30 years old at 16 comprehensive health service centers in Tehran, Iranwho participated in a diabetes screening program. The dataset was balanced using random sampling and the synthetic minorityover-sampling technique (SMOTE). The dataset was split into training set (80%) and test set (20%). Shapley valueswere calculated to select the most important features. Noise analysis was performed by adding Gaussian noise to the numericalfeatures to evaluate the robustness of feature importance. Five different machine learning algorithms, including CatBoost,random forest, XGBoost, logistic regression, and an artificial neural network, were used to model the dataset. Accuracy,sensitivity, specificity, accuracy, the F1-score, and the area under the curve were used to evaluate the model. Results: Age,waist-to-hip ratio, body mass index, and systolic blood pressure were the most important factors for predicting fasting bloodglucose status. Though the models achieved similar predictive ability, the CatBoost model performed slightly better overallwith 0.737 area under the curve (AUC). Conclusions: A gradient boosted decision tree model accurately identified the mostimportant risk factors related to diabetes. Age, waist-to-hip ratio, body mass index, and systolic blood pressure were the mostimportant risk factors for diabetes, respectively. This model can support planning for diabetes management and prevention.
Abstract
Objectives: This study aimed to develop a model to predict fasting blood glucose status using machine learning and datamining, since the early diagnosis and treatment of diabetes can improve outcomes and quality of life. Methods: This crosssectionalstudy analyzed data from 3376 adults over 30 years old at 16 comprehensive health service centers in Tehran, Iranwho participated in a diabetes screening program. The dataset was balanced using random sampling and the synthetic minorityover-sampling technique (SMOTE). The dataset was split into training set (80%) and test set (20%). Shapley valueswere calculated to select the most important features. Noise analysis was performed by adding Gaussian noise to the numericalfeatures to evaluate the robustness of feature importance. Five different machine learning algorithms, including CatBoost,random forest, XGBoost, logistic regression, and an artificial neural network, were used to model the dataset. Accuracy,sensitivity, specificity, accuracy, the F1-score, and the area under the curve were used to evaluate the model. Results: Age,waist-to-hip ratio, body mass index, and systolic blood pressure were the most important factors for predicting fasting bloodglucose status. Though the models achieved similar predictive ability, the CatBoost model performed slightly better overallwith 0.737 area under the curve (AUC). Conclusions: A gradient boosted decision tree model accurately identified the mostimportant risk factors related to diabetes. Age, waist-to-hip ratio, body mass index, and systolic blood pressure were the mostimportant risk factors for diabetes, respectively. This model can support planning for diabetes management and prevention.
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