A hybrid diabetes risk prediction model XGB-ILSO-1DCNN

Abstract

Accurately predicting the risk of diabetes is of paramount importance for early intervention and prevention. To achieve precise diabetes risk prediction, we propose a hybrid diabetes risk prediction model, XGB-ILSO-1DCNN, which combines the Extreme Gradient Boosting (XGBoost) algorithm, the Improved Lion Swarm Optimization algorithm, and the deep learning model 1DCNN. Firstly, an XGBoost is trained based on the raw data and the prediction result based on XGBoost is regarded as a new feature, concatenating it with the original features to form a new feature set. Then, we introduce a hybrid approach called ILSO-1DCNN, which is based on improved Lion Swarm Optimization (ILSO) and one-dimensional convolutional neural network (1DCNN). This approach is proposed for diabetes risk prediction. The ILSO-1DCNN algorithm utilizes the optimization capabilities of ILSO to automatically determine the hyperparameters of the 1DCNN network. Finally, we conducted comprehensive experiments on the PIMA dataset and compared our model with baseline models. The experimental results not only demonstrate our model's exceptional predictive performance across various evaluation criteria but also highlight its efficiency and low complexity. This study introduces a novel and effective diabetes risk prediction approach, making it a valuable tool for clinical analysis in the care of diabetic patients.