Chakrabarty, AmitabhaHasan, KamrulIslam, SafkatSamio, Md. Mehfil Rashid Khan2018-05-102018-05-1020182018-04ID 13101102ID 13301099ID 13101029http://hdl.handle.net/10361/10119Cataloged from PDF version of thesis.Includes bibliographical references (pages 46-48).This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.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.48 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.OrthopedicHealth conditionMachine learningDecision treeAlgorithmThesis