Improvisation of classification performance based on feature optimization for differentiation of Parkinson’s disease from other neurological diseases using gait characteristics

Most neurological disorders that include Parkinson’s disease (PD) as well as other neurological diseases such as Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD) have some common abnormalities regarding the movement, vocal, and cognitive behaviors of sufferers. Variations in the manifestation of these types of abnormality help distinguish one disorder from another. In this study, differentiation was performed based on the gait characteristics of patients afflicted by different neurological disorders. In the recent past, many researchers have applied different machine learning and feature selection techniques to the classification of different groups of patients based on common abnormalities. However, in an era of modernization where the focus is on timely low-cost automatization and pattern recognition, such techniques require improvisation to provide high performance. We attempted to improve the performance of such techniques using different feature optimization methods, such as a genetic algorithm (GA) and principal component analysis (PCA), and applying different classification approaches, i.e., linear, nonlinear, and probabilistic classifiers. In this study, gait dynamics data of patients suffering with PD, ALS, and HD were collated from a public database, and a binary classification approach was used by taking PD as one group and adopting ALS+HD as another group. Performance comparison was achieved using different classification techniques that incorporated optimized feature sets obtained from GA and PCA. In comparison with other classifiers using different feature sets, the highest accuracy (97.87%) was obtained using random forest combined with GA-based feature sets. The results provide evidence that could assist medical practitioners in differentiating PD from other neurological diseases using gait characteristics

Korea’s technical assistance for better governance

노트 : - Paper for International Conference on U.S.-Korea Dialogue on Strategies for Effective Development Cooperation - Organized by Asia Foundation October 17-18, 2011 Seoul, Korea 행사명 : International Conference on U.S.-Korea Dialogue on Strategies for Effective Development Cooperatio

Transcanal Endoscopic Ear Surgery for Congenital Cholesteatoma

Objectives As endoscopic instrumentation, techniques and knowledges have significantly improved recently, endoscopic ear surgery has become increasingly popular. Transcanal endoscopic ear surgery (TEES) can provide better visualization of hidden areas in the middle ear cavity during congenital cholesteatoma removal. We aimed to describe outcomes for TEES for congenital cholesteatoma in a pediatric population. Methods Twenty-five children (age, 17 months to 9 years) with congenital cholesteatoma confined to the middle ear underwent TEES by an experienced surgeon; 13 children had been classified as Potsic stage I, seven as stage II, and five as stage III. The mean follow-up period was 24 months. Recurrence of congenital cholesteatoma and surgical complication was observed. Results Congenital cholesteatoma can be removed successfully via transcanal endoscopic approach in all patients, and no surgical complications occurred; only one patient with a stage II cholesteatoma showed recurrence during the follow-up visit, and the patient underwent revision surgery. The other patients underwent one-stage operations and showed no cholesteatoma recurrence at their last visits. Two patients underwent second-stage ossicular reconstruction. Conclusion Although the follow-up period and number of patients were limited, pediatric congenital cholesteatoma limited to the middle ear cavity could be safely and effectively removed using TEES