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- W2801771282 abstract "A person’s orthopedic health condition can be detected from his biomechanical features. Now a days, disease prediction can be done automatically. Application of machine learning algorithms in medical science is not new. Different algorithms are applied to detect diseases and classify patients accordingly. This paper aims to assist specialists to predict the type of orthopedic disease. In this paper we have applied various machine learning algorithms to find out how each algorithm performs to detect and classify orthopedic patients. Each of the patient in the dataset is represented by six biomechanical attributes derived from the shape and orientation of pelvis and lumbar spine. We performed our operation in two stages and got an average accuracy of more than 90 percent for most of the algorithms, whereas Decision Tree (DT) algorithm stood out from the rest providing 99 percent accuracy." @default.
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- W2801771282 date "2018-06-01" @default.
- W2801771282 modified "2023-09-22" @default.
- W2801771282 title "A Machine Learning Approach on Classifying Orthopedic Patients Based on Their Biomechanical Features" @default.
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- W2801771282 doi "https://doi.org/10.1109/iciev.2018.8641042" @default.
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