Volume 18, No. 6, 2021

Comparative Analysis Of Supervised Machine Learning Algorithms For Prediction Of Orthopedic Disease

Dr.D.UmaDevi , Dr.D.N.D.Harini , Dr.Ch.Sita Kumari , Mohammed Afeeda


This study presents the classification of orthopedic disease patients by using the lumber and pelvic state information. Orthopedic diseases are common for people of all ages in the present day. In this study, a dataset gathered from Kaggle containing data of 310 patients with six biomechanical features describing the state of patients. Machine learning algorithms play a vital role in designing high-performance diagnosis systems and the prediction of diseases. For this purpose Logistic Regression, K-Nearest Neighbor, Random forest classifier, Decision Tree classifier algorithms are applied to the dataset. The algorithm results are then compared, the one that furnished the best result with an accuracy of 97 percent is seen as Decision Tree when compared to other algorithms' accuracy.

Pages: 851-860

Keywords: Machine-learning algorithms, Orthopedic disease, Accuracy, Classification, Prediction

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