The Use of Machine Learning Models with Optuna in Disease Prediction
Lee‐Lun Lai, Ying-Lei Lin, Yuhui Liu, Jung-Pin Lai, Wen‐Chieh Yang, Hung-Pin Hou, Ping‐Feng Pai
Abstract
Effectively and equitably allocating medical resources, particularly for minority groups, is a critical issue that warrants further investigation in rural hospitals. Machine learning techniques have gained significant traction and demonstrated strong performance across various fields in recent years. The determination of hyperparameters significantly influences the performance of machine learning models. Thus, this study employs Optuna, a framework specifically designed for optimizing the hyperparameters of machine learning models. Building on prior research, machine learning models with Optuna (MLOPTA) are introduced to forecast diseases of indigenous patients. The numerical results reveal that the designed MLOPTA system can accurately capture the occurrences of specified diseases. Therefore, the MLOPTA system offers a promising approach for disease forecasting. The disease forecasting results can serve as crucial references for allocating hospital resources.