Forecasting A Cycling Distance based on Personal Health Data with Hybrid GA-SVR Approach
The aim of this study is to forecast the optimal cycling distance of 30 minutes approximately for physically constrained individuals, especially those having a congenital disease or elderly person. Data illustrations are collected from having 94 people who cycled in Ladkrabang by questionnaires. The research focuses on forecasting optimal distances by exploiting Support Vector Regression based on Genetic Algorithm (GA-SVR) that will be employed to improve the accuracy of the numerical study. We offer performance comparisons of our model against Multiple Linear Regression (MLR) and Support Vector Regression (SVR) algorithms. The experimental results demonstrate that GA-SVR outperforms MLR and SVR based on the Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). This GA-SVR model is proven to be an effective approach to predict cycling distances.