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    • Thesis & Report, BSc (Computer Science and Engineering)
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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    A machine learning approach on classifying orthopedic patients based on their biomechanical features

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    13101102,13301099,13101029_CSE.pdf (1.492Mb)
    Date
    2018-04
    Publisher
    BRAC University
    Author
    Hasan, Kamrul
    Islam, Safkat
    Samio, Md. Mehfil Rashid Khan
    Metadata
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    URI
    http://hdl.handle.net/10361/10119
    Abstract
    A person’s orthopedic health condition can be detected from his biomechanical features. 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 which one works most accurately to detect and classify orthopedic patients. Each of the patients 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% accuracy.
    Keywords
    Orthopedic; Health condition; Machine learning; Decision tree; Algorithm
     
    Description
    This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
     
    Cataloged from PDF version of thesis.
     
    Includes bibliographical references (pages 46-48).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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