Deep learning for predicting drug-related problems in diabetes patients

Abstract

Machine learning and deep learning have made advances in the healthcare domain. In this study, we aim to apply deep learning models to predict the drug-related problems (DRPs) status for diabetes patients. Also, to determine the appropriate model to use for classification using deep learning algorithms or machine learning methods to investigate which one performed better results for tabular data by comparing the achieved deep learning results with the machine learning methods to figure out which one gives better results. To apply the deep learning models, the same criteria that were applied in the previous study have been implemented in this investigation, and the same dataset was used. The results show that the machine learning algorithms especially the random forests for predicting the DRPs status outperform the deep learning models. For classification tasks in healthcare for tabular data, the findings of this study show that machine learning methods are the appropriate model instead of using deep learning to apply classification.