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Source: Sensors
Condition: Depression
Education: Learning
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Total 1 results found since Jan 2013.
Sensors, Vol. 23, Pages 7946: Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients
In this study, we aimed to develop a machine learning (ML) model to predict the risk of PSAMO. We retrospectively studied 1780 patients with stroke who were divided into PSAMO vs. no PSAMO groups based on results of validated depression and anxiety questionnaires. The features collected included demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. Recursive feature elimination was used to select features to input in parallel to eight ML algorithms to train and test the model. Bayesian optimization was used for hyperparameter tuning. Shaple...
Source: Sensors - September 17, 2023 Category: Biotechnology Authors: Chien Wei Oei Eddie Yin Kwee Ng Matthew Hok Shan Ng Ru-San Tan Yam Meng Chan Lai Gwen Chan Udyavara Rajendra Acharya Tags: Communication Source Type: research
