Alcohol User Prediction With Deep Learning Methods From Electronic Health Record Data

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Harran Üniversitesi Mühendislik Dergisi
Cilt: 10 Sayı: 2
Alcohol User Prediction With Deep Learning Methods From Electronic Health Record Data

Alcohol User Prediction With Deep Learning Methods From Electronic Health Record Data

Pages : 94-104

View : 63 | Download : 43

Publication Date : 2025-06-30

Article Type : Research Paper

Abstract :Alcohol consumption has negative effects on individuals and societies in various areas, including health, economic, social and cultural aspects. Alcohol use prediction is a very important research topic to prevent the negative effects of alcohol. While dose-dependent alcohol use disorder is usually predicted in the literature, in this study, unlike the literature, dose-independent alcohol users are predicted. This prediction is made from electronic health record data using popular deep learning methods. The dataset used in the study consists of 24 different attributes including personal characteristics and health parameters of 991346 individuals collected from the National Health Insurance Service in Korea. The data were optimised after digitisation and normalisation preprocessing steps. A certain amount of training and test separation was applied to the dataset. Then, an alcohol user prediction model was developed using artificial neural networks, LSTM and CNN method. According to the results obtained, although the models achieved close prediction success, artificial neural networks achieved the best result. After artificial neural networks, CNN ranked second, and LSTM ranked last. By using more than one deep learning method together in the study, a conclusion about the general success of deep learning methods on the current problem has been made and a method that will make an important contribution to the solution of the problem has been put forward.
Keywords : Alkol kullanıcısı, Derin öğrenme, LSTM, CNN, ANN

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