genetic programming

The forecasting of daily outpatient visits has significant practical implications in outpatient clinic operation management, not only contributing to guiding long-term resource planning and scheduling but also making tactical resolutions for short-term adjustments on special days such as holidays. We here in propose an effective genetic programming (GP)-based forecasting model to predict daily outpatient visits (OV) in a primary hospital.

In the GP-based model, the holiday-based distance outlier mining algorithm was used to determine the holiday effect. In addition, solar terms were applied as the smallest unit to more accurately determine the impact of a change in the climate on the outpatient volume. A segmental learning strategy also was used to predict the daily outpatient volume for the time series data.

The GP-based prediction could more effectively extract depth information from a finite training sample size and achieve a better performance for predicting daily outpatient visits, with lower root mean square error (RMSE) and higher coefficient of determination (R2) values, than the seasonal autoregressive integrated moving average (SARIMA) model in the time range of holidays and the holiday effect.

GP-based model can achieve better prediction performance by overcoming the shortcomings of the SARIMA model. The results can be applied to support decision-making and planning of outpatient clinic resources, to help managers implement periodic scheduling of available resources on the basis of periodic features, and to perform proactive scheduling of additional resources.

Diabetes is a global health challenge that cusses high incidence of major social and economic consequences. As such, early prevention or identification of those people at risk is crucial for reducing the problems caused by it. The aim of study was to extract the rules for diabetes diagnosing using genetic programming.

This study utilized the PIMA dataset of the university of California, Irvine. This dataset consists of the information of 768 Pima heritage women, including 500 healthy persons and 268 persons with diabetes. Regarding the missing values and outliers in this dataset, the K-nearest neighbor and k-means methods are applied respectively. Moreover, a genetic programming model (GP) was conducted to diagnose diabetes as well as to determine the most important factors affecting it. Accuracy, sensitivity and specificity of the proposed model on the PIMA dataset were obtained as 79.32, 58.96 and 90.74%, respectively.

The experimental results of our model on PIMA revealed that age, PG concentration, BMI, Tri Fold thick and Serum Ins were effective in diabetes mellitus and increased risk of diabetes. In addition, the good performance of the model coupled with the simplicity and comprehensiveness of the extracted rules is also shown by the experimental results.

GPs can effectively implement the rules for diagnosing diabetes. Both BMI and PG concentration are also the most important factors to increase the risk of suffering from diabetes.