Genetic Algorithm Optimization for Hybrid Deep Learning Prognosis of Reverse Total Shoulder Arthroplasty Rehabilitation

Abstract

Within the framework of the ongoing development of application of Machine Learning models in Medicine and Physical Therapy, the development of accurate prognosis algorithms for postoperative patients during the rehabilitation phase remains an area requiring further refinement. This paper examines hybrid Deep Learning models that integrate Convolution Neural Networks, Long Short-Term Memory and Gated Recurrent Unit networks, as well as genetic algorithm optimization for feature selection for predicting the time needed for a patient to rehabilitate. Patient data included features like age, passive range of available movements (preoperative and postoperative) and total rehabilitation time. Genetic Algorithm optimization for feature selection indicated that 4 out of the 16 available features are adequate for predicting rehabilitation time. Hybrid Deep Learning models achieved a Root Mean Squared Error (RMSE) of 12 days (less than 0.4 months) in rehabilitation time prediction, demonstrating good performance on a relatively small dataset of 120 patients.

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