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Storm surge is a genuine common fiasco coming from the ocean. Therefore, an exact forecast of surges is a vital assignment to dodge property misfortunes and to decrease a chance caused by tropical storm surge. Genetic Programming (GP) is an evolution-based model learning technique that can simultaneously find the functional form and the numeric coefficients for the model. Therefore, GP has been widely applied to build models for predictive problems. However, GP has seldom been applied to the problem of storm surge forecasting. In this paper, we propose a new method to use GP for evolving models for storm surge forecasting. Experimental results on datasets collected from the Tottori coast of Japan show that GP can evolve accurate storm surge forecasting models. Moreover, GP can automatically select relevant features when evolving storm surge forecasting models, and the models evolved by GP are interpretable. • The paper proposes a new approach to using Genetic Programming (GP) to evolve models for storm surge forecasting. • GP can evolve more accurate models for storm surge forecasting than other existing machine learning methods. • The model evolved by GP is more interpretable than models evolved by other (black-box) methods such as neural networks. • GP can automatically select relevant features when evolving storm surge forecasting models. [ABSTRACT FROM AUTHOR]

• Information from electronic health records can be used to help us understand what contributes to the onset of diseases including type 2 diabetes mellitus. • Machine learning, including deep learning, has been used to predict the onset of diseases using information from electronic health records. • Our work is the first to use wide and deep learning, a state-of-the-art deep learning architecture that achieves both memorisation and generalisation abilities, to predict the onset of type 2 diabetes mellitus using electronic health records. • Our algorithm is better at predicting the onset of type 2 diabetes mellitus than other state-of-the-art machine learning algorithms using the same dataset with similar experimental settings. • The synthetic minority over-sampling technique (SMOTE) was found to work better with the wide and deep learning framework than other machine learning algorithms in improving sensitivity for imbalanced electronic health record datasets. Diabetes is responsible for considerable morbidity, healthcare utilisation and mortality in both developed and developing countries. Currently, methods of treating diabetes are inadequate and costly so prevention becomes an important step in reducing the burden of diabetes and its complications. Electronic health records (EHRs) for each individual or a population have become important tools in understanding developing trends of diseases. Using EHRs to predict the onset of diabetes could improve the quality and efficiency of medical care. In this paper, we apply a wide and deep learning model that combines the strength of a generalised linear model with various features and a deep feed-forward neural network to improve the prediction of the onset of type 2 diabetes mellitus (T2DM). The proposed method was implemented by training various models into a logistic loss function using a stochastic gradient descent. We applied this model using public hospital record data provided by the Practice Fusion EHRs for the United States population. The dataset consists of de-identified electronic health records for 9948 patients, of which 1904 have been diagnosed with T2DM. Prediction of diabetes in 2012 was based on data obtained from previous years (2009–2011). The imbalance class of the model was handled by Synthetic Minority Oversampling Technique (SMOTE) for each cross-validation training fold to analyse the performance when synthetic examples for the minority class are created. We used SMOTE of 150 and 300 percent, in which 300 percent means that three new synthetic instances are created for each minority class instance. This results in the approximated diabetes:non-diabetes distributions in the training set of 1:2 and 1:1, respectively. Our final ensemble model not using SMOTE obtained an accuracy of 84.28%, area under the receiver operating characteristic curve (AUC) of 84.13%, sensitivity of 31.17% and specificity of 96.85%. Using SMOTE of 150 and 300 percent did not improve AUC (83.33% and 82.12%, respectively) but increased sensitivity (49.40% and 71.57%, respectively) with a moderate decrease in specificity (90.16% and 76.59%, respectively). Our algorithm has further optimised the prediction of diabetes onset using a novel state-of-the-art machine learning algorithm: the wide and deep learning neural network architecture. [ABSTRACT FROM AUTHOR]