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- W4310607250 abstract "Pain is widely acknowledged to be a complicated experience. Pain is also a symptom of joint inflammation in arthritis, such as rheumatoid arthritis and osteoarthritis. Machine learning (ML) for classification uses nonparametric supervised learning techniques called decision trees. By gaining knowledge from decision rules created from the characteristics of the supplied data, they may be utilized to forecast the target variable. We applied a machine learning decision model to anticipate j oint pain (target variable) while considering clinical biochemistry. A total of 650 patients were included who visited orthopedic OPD with joint swelling or myalgia. Decision Tree was trained tested, and cross-validated with supervised learning. The model was evaluated along with the selected features/attributes (age, gender, uric acid, & CRP). 44% of patients were diagnosed with joint pain. The decision tree model yielded an accuracy of94% and a validation accuracy of 96%. Uric acid was strongly correlated with joint pain. Early ML-based joint pain identification will help avert more significant orthopedic issues." @default.
- W4310607250 created "2022-12-12" @default.
- W4310607250 creator A5020781112 @default.
- W4310607250 creator A5091024712 @default.
- W4310607250 date "2022-10-31" @default.
- W4310607250 modified "2023-09-27" @default.
- W4310607250 title "Application of Machine Learning Decision Tree in Diagnosing Joint Pain" @default.
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- W4310607250 doi "https://doi.org/10.1109/tiptekno56568.2022.9960183" @default.
- W4310607250 hasPublicationYear "2022" @default.
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