A new hybrid method based on local fisher discriminant analysis and support vector machines for hepatitis disease diagnosis

In this paper, a novel hybrid method named the LFDA_SVM, which integrates a new feature extraction method and a classification algorithm, has been introduced for diagnosing hepatitis disease. The two integrated methods are the local fisher discriminant analysis (LFDA) and the supporting vector machine (SVM), respectively. In the proposed LFDA_SVM, the LFDA is employed as a feature extraction tool for dimensionality reduction in order to further improve the diagnostic accuracy of the standard SVM algorithm. The effectiveness of the LFDA_SVM has been rigorously evaluated against the hepatitis dataset, a benchmark dataset, from UCI Machine Learning Database in terms of classification accuracy, sensitivity and specificity respectively. In addition, the proposed LFDA_SVM has been compared with three existing methods including the SVM based on principle component analysis (PCA_SVM), the SVM based on fisher discriminant analysis (FDA_SVM) and the standard SVM in terms of their classification accuracy. Experimental results have demonstrated that the LFDA_SVM greatly outperforms other three methods. The best classification accuracy (96.77%) obtained by the LFDA_SVM is much higher than that of the compared ones. Promisingly, the proposed LFDA_SVM might serve as a new candidate of powerful methods for diagnosing hepatitis with excellent performance.