JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
Orthopedic Disease Detection Based on Lumber and Pelvic State of Patients.
Punyashree M, Santhosh S G
The accurate and timely diagnosis of orthopedic diseases is crucial for effective treatment and patient care. Diagnosing and treating orthopedic diseases, especially those affecting the lumbar and pelvic regions, can be quite difficult. The goal of this research is to apply the k-nearest neighbor algorithm to develop a structure for the prediction of orthopedic diseases. The target is to improve intervention as well as diagnostic accuracy using the medical report of the patient. This research involves the gathering of a dataset that covers clinical data on orthopedic diseases. To increase the k-NN model's performance, preprocessing and enhancement of the dataset are done. It is possible to distinguish between normal and abnormal conditions because of the proposed k-NN architecture, which is made to extract essential data from the patient's report. In order to ensure great reliability and accuracy, the system is put through extensive training and validation using advanced techniques. The system includes a predictive component that analyses the risk of disease progression, providing useful data for proactive healthcare management. According to the results, the k-NN-based framework provides an accurate means to support healthcare providers in the identification of orthopedic diseases. This approach may speed up medications, reduce diagnostic errors, and eventually enhance patient outcomes.
Pelvic, Lumber, Machine Learning, Prediction, K-Nearest Neighbor (KNN).