PREDICTING RETURN TO WORK AFTER CARDIAC REHABILITATION USING MACHINE LEARNING MODELS

Objective: To explore machine learning models for predicting return to work after cardiac rehabilitation. Subjects: Patients who were admitted to the Univer-sity of Malaya Medical Centre due to cardiac events.Methods: Eight different machine learning models were evaluated. The models included 3 different sets of features: full features; significant featu-res from multiple logistic regression; and features selected from recursive feature extraction techni-que. The performance of the prediction models with each set of features was compared.Results: The AdaBoost model with the top 20 fea-tures obtained the highest performance score of 92.4% (area under the curve; AUC) compared with other prediction models.Conclusion: The findings showed the potential of using machine learning models to predict return to work after cardiac rehabilitation.

Automated summary

This study explores the potential of using machine learning models to predict return to work after cardiac rehabilitation. By comparing the performance of different prediction models with different sets of features, the findings show that the AdaBoost model with the top 20 features achieved the highest performance score.