Development of a care plan recommendation system for physical therapy patients

Abstract

Physical therapy includes many different treatment methods for patients' musculoskeletal problems. However, each patient has different complaints, lifestyles, and recovery rates. The importance of personalized care in physical therapy practices has therefore increased. The aim of this study is to develop a personalized recommendation system for patients' physical therapy processes. A dataset of 12,393 patients was collected from a private hospital and analyzed using machine learning and deep learning algorithms. Prediction was performed with Multi-Layer Perceptron (MLP) Classifier, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Feedforward Neural Network (FNN) models using 5-fold cross-validation. Among single models, the ANN achieved the best performance with Precision, Recall, F1-Score, and Accuracy values of 0.8733, 0.8666, 0.8600, and 0.8666, respectively. In the ensemble stage, a Voting Classifier combining Random Forest and LightGBM achieved 96% accuracy for predicting treatment session numbers, while the stacked-ensemble pipeline integrating ANN and CNN achieved 86% success in personalized treatment plan recommendation. The novelty of this study lies in the design of a stacked ensemble pipeline that integrates treatment session prediction with personalized care plan recommendation, enhancing both effectiveness and patient-centeredness in physical therapy. Each patient can thus receive the most appropriate treatment for their health status and needs. Personalized care can lead to faster recovery, greater participation in treatment processes, and more positive long-term outcomes. This study contributes to making physical therapy more effective and patient-centered by emphasizing the importance of personalized care in healthcare.

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