J Med Syst - SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease.

Tópicos

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Resumo

Parkinson disease (PD) is an age-related deterioration of certain nerve systems, which affects movement, balance, and muscle control of clients. PD is one of the common diseases which affect 1% of people older than 60?years. A new classification scheme based on support vector machine (SVM) selected features to train rotation forest (RF) ensemble classifiers is presented for improving diagnosis of PD. The dataset contains records of voice measurements from 31 people, 23 with PD and each record in the dataset is defined with 22 features. The diagnosis model first makes use of a linear SVM to select ten most relevant features from 22. As a second step of the classification model, six different classifiers are trained with the subset of features. Subsequently, at the third step, the accuracies of classifiers are improved by the utilization of RF ensemble classification strategy. The results of the experiments are evaluated using three metrics; classification accuracy (ACC), Kappa Error (KE) and Area under the Receiver Operating Characteristic (ROC) Curve (AUC). Performance measures of two base classifiers, i.e. KStar and IBk, demonstrated an apparent increase in PD diagnosis accuracy compared to similar studies in literature. After all, application of RF ensemble classification scheme improved PD diagnosis in 5 of 6 classifiers significantly. We, numerically, obtained about 97% accuracy in RF ensemble of IBk (a K-Nearest Neighbor variant) algorithm, which is a quite high performance for Parkinson disease diagnosis.

Resumo Limpo

parkinson diseas pd agerel deterior certain nerv system affect movement balanc muscl control client pd one common diseas affect peopl older year new classif scheme base support vector machin svm select featur train rotat forest rf ensembl classifi present improv diagnosi pd dataset contain record voic measur peopl pd record dataset defin featur diagnosi model first make use linear svm select ten relev featur second step classif model six differ classifi train subset featur subsequ third step accuraci classifi improv util rf ensembl classif strategi result experi evalu use three metric classif accuraci acc kappa error ke area receiv oper characterist roc curv auc perform measur two base classifi ie kstar ibk demonstr appar increas pd diagnosi accuraci compar similar studi literatur applic rf ensembl classif scheme improv pd diagnosi classifi signific numer obtain accuraci rf ensembl ibk knearest neighbor variant algorithm quit high perform parkinson diseas diagnosi

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