Biomechanics Features-Based IoT and Machine Learning for Orthopedic Patients

Abstract

The chapter focuses on classifying orthopedic patients using biomechanical parameters obtained from the form and position of the pelvis and lumbar spine. Depending on the objective, the authors use machine learning algorithms to classify patients into one of two or three groups. In addition, they do exploratory data analysis to better understand the correlations between biomechanical parameters and patient classifications. The findings of the study could have significant consequences for the diagnosis and management of orthopedic diseases. The study could go through the ramifications of applying machine learning models in orthopedic patient classification, such as model accuracy, potential biases, and ethical concerns. Exploratory data analysis (EDA) can be used in addition to machine learning classification to get insights into the data. This can assist in identifying any patterns or outliers in the data that may have an impact on the performance of the machine learning models. Furthermore, feature selection approaches can be used to determine the most essential properties for classification, potentially boosting the models’ accuracy and efficiency.

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