Hide/Show Apps

‘oagait’ : a decision support system for grading knee osteoarthritis using gait data

Şen Köktaş, Nigar
Gait analysis is the process of collecting and analyzing quantitative information about walking patterns of the people. Gait analysis enables the clinicians to differentiate gait deviations objectively. Diagnostic decision making from gait data only requires high level of medical expertise of neuromusculoskeletal system trained for the purpose. An automated system is expected to decrease this requirement by a ‘transformed knowledge’ of these experts. This study presents a clinical decision support system for the detecting and scoring of a knee disorder, namely, Osteoarthritis (OA). Data used for training and recognition is mainly obtained through Computerized Gait Analysis software. Sociodemographic and disease characteristics such as age, body mass index and pain level are also included in decision making. Subjects are allocated into four OA-severity categories, formed in accordance with the Kellgren-Lawrence scale: “Normal”, “Mild”, “Moderate”, and “Severe”. Different types of classifiers are combined to incorporate the different types of data and to make the best advantages of different classifiers for better accuracy. A decision tree is developed with Multilayer Perceptrons (MLP) at the leaves. This gives an opportunity to use neural networks to extract hidden (i.e., implicit) knowledge in gait measurements and use it back into the explicit form of the decision trees for reasoning. Individual feature selection is applied using the Mahalanobis Distance measure and most discriminatory features are used for each expert MLP. Significant knowledge about clinical recognition of the OA is derived by feature selection process. The final system is tested with test set and a success rate of about 80% is achieved on the average.