Comput Methods Programs Biomed - Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms.

Tópicos

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Resumo

Improving accuracies of machine learning algorithms is vital in designing high performance computer-aided diagnosis (CADx) systems. Researches have shown that a base classifier performance might be enhanced by ensemble classification strategies. In this study, we construct rotation forest (RF) ensemble classifiers of 30 machine learning algorithms to evaluate their classification performances using Parkinson's, diabetes and heart diseases from literature. While making experiments, first the feature dimension of three datasets is reduced using correlation based feature selection (CFS) algorithm. Second, classification performances of 30 machine learning algorithms are calculated for three datasets. Third, 30 classifier ensembles are constructed based on RF algorithm to assess performances of respective classifiers with the same disease data. All the experiments are carried out with leave-one-out validation strategy and the performances of the 60 algorithms are evaluated using three metrics; classification accuracy (ACC), kappa error (KE) and area under the receiver operating characteristic (ROC) curve (AUC). Base classifiers succeeded 72.15%, 77.52% and 84.43% average accuracies for diabetes, heart and Parkinson's datasets, respectively. As for RF classifier ensembles, they produced average accuracies of 74.47%, 80.49% and 87.13% for respective diseases. RF, a newly proposed classifier ensemble algorithm, might be used to improve accuracy of miscellaneous machine learning algorithms to design advanced CADx systems.

Resumo Limpo

improv accuraci machin learn algorithm vital design high perform computeraid diagnosi cadx system research shown base classifi perform might enhanc ensembl classif strategi studi construct rotat forest rf ensembl classifi machin learn algorithm evalu classif perform use parkinson diabet heart diseas literatur make experi first featur dimens three dataset reduc use correl base featur select cfs algorithm second classif perform machin learn algorithm calcul three dataset third classifi ensembl construct base rf algorithm assess perform respect classifi diseas data experi carri leaveoneout valid strategi perform algorithm evalu use three metric classif accuraci acc kappa error ke area receiv oper characterist roc curv auc base classifi succeed averag accuraci diabet heart parkinson dataset respect rf classifi ensembl produc averag accuraci respect diseas rf newli propos classifi ensembl algorithm might use improv accuraci miscellan machin learn algorithm design advanc cadx system

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