Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on DCT and 2DLDA
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
Computerized bone age assessment using DCT and LDA
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
An Automated Approach to the Design of Decision Tree Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error estimation in pattern recognition via -distance between posterior density functions
IEEE Transactions on Information Theory
Local linear perceptrons for classification
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Nowadays, researchers try to introduce a more convenient and fast approach (especially a non-invasive one) to diagnose diseases fast and more precisely. Some patient death may be because of wrong diagnosis. Some patients that suffer diseases such as Parkinson disease are known to be dead of wrong diagnosis. Such an approach would help physicians to focus on the correct disease and its treatment and to avoid wasting precious time - that may be critical for the patient - on diagnosis. In this study, we try to develop a new automated approach for classifying (diagnosing) locomotive patients using features that may be extracted from their gait signal. We selected four groups: patients with Huntington's disease, Parkinson's disease and Amyotrophic Lateral Sclerosis and a group of healthy subjects. Examining different available classifiers on all proposed features, we have introduced a novel feature with acceptably low error rate using quadratic Bayes classifier.