DETECTION OF KNEE OSTEOARTHRITIS BASED ON CENTER OF PRESSURE DATA AND THE BAT ALGORITHM

The high rate of knee osteoarthritis has raised the need for accurate diagnostic methods. In this study, we propose a precise detection method using the center of pressure data obtained from the patients. The introduced automatic detection pipeline is based on the two modern algorithms of grey wolf and BAT. The extracted statistical features and the obtained data from healthy individuals and patients are processed with the grey wolf binary algorithm. The results are fed into the binary bat algorithm to select important features and increase the pipeline accuracy. Then the groups are classified using a four-layer neural network. We show that the proposed method with a simple four-layer neural network offers fantastic accuracy in high-speed processing large data and classifies the high-dimensional knee osteoarthritis center of pressure data with appropriate precision, recall, specificity, and F1 values. The proposed method has direct applications in knee osteoarthritis diagnostics in clinics.

Automated summary

In this study, a precise detection method using center of pressure data obtained from patients is proposed. The introduced automatic detection pipeline is based on the grey wolf and BAT algorithms. Statistical features and data from healthy individuals and patients are processed using the grey wolf binary algorithm, and the results are inputted into the binary bat algorithm for feature selection and improved accuracy. The groups are then classified using a four-layer neural network. The proposed method offers fantastic accuracy in high-speed processing large data and classifying high-dimensional knee osteoarthritis center of pressure data with appropriate precision, recall, specificity, and F1 values. It has direct applications in knee osteoarthritis diagnostics in clinics.