Efficient Disease Classifier Using Data Mining Techniques: Refinement of Random Forest Termination Criteria

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 Efficient Disease Classifier Using Data Mining Techniques: Refinement of Random Forest Termination Criteria

Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2013, Vol 14, Issue 5

Abstract

 In biomedical field, the classification of disease using data mining is the critical task. The prediction accuracy plays a vital role in disease data set. More data mining classification algorithms like decision trees, neural networks, Bayesian classifiers are used to diagnosis the diseases. In decision tree Random Forest, Initially a forest is constructed from ten tress. The accuracy is measured and compared with desired accuracy. If the selected best split of trees matched the desired accuracy the construction terminates. Otherwise a new tree is added with random forest and accuracy is measured. The fitting criteria of random forest are accuracy and correlation. The accuracy is based on the mean absolute percentage error (MAPE) and the mean absolute relative error (MARE).In proposed system to refine the termination criteria of Random Forest, Binomial distribution, multinomial distribution and sequential probability ratio test (SPRT) are used. The proposed method stops the random forest earlier compared with existing Random Forest algorithm. The supervised learning model like support vector machine takes a set of inputs and analyze the inputs and recognize the desired patterns. The disease data sets are supplied to SVM and prediction accuracy is measured. The comparison is made between Random Forest and SVM and best class labels are identified based on disease.

Authors and Affiliations

K. Kalaiselvi

Keywords

SVM Random Forest disease classification

EP ID EP93784
DOI -
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