A Study on Hepatitis Disease Diagnosis Using Probabilistic Neural Network

Bascil, M. Serdar; Öztekin, Halit

doi:10.1007/s10916-010-9621-x

Cited by 44 publications

(21 citation statements)

References 15 publications

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“…Furthermore, the classification outcomes of 3SVM approach are compared with results of other published approaches. Table IV lists comparisons of 30 methods proposed in literature as listed in [6], [7], [23] and [4]. From comparisons, the 3SVM gives the better results than other methods proposed in literature, where the 3SVM enhances the performance of classification and the accuracy rate increases with 2.5%, 1.98% and 7.5% from the recently published methods [32], [7] and [4].…”

Section: A Results and Discussionmentioning

confidence: 96%

“…The obtained accuracy is (96.25%), which the best accuracy rate when compared with other methods. In recent study presented by [4], the authors summarized the most works in the area of hepatitis disease diagnosis, and proposed a new method by employing Probabilistic Neural Network structure called PNN (10xFC), the results that obtained is 91.25%.…”

Section: Introductionmentioning

confidence: 99%

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SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis

Afif¹,

Hedar²,

Hamid³

et al. 2013

IJACSA

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Abstract-In this paper, a new classification approach combining support vector machine with scatter search approach for hepatitis disease diagnosis is presented, called 3SVM. The scatter search approach is used to find near optimal values of SVM parameters and its kernel parameters. The hepatitis dataset is obtained from UCI. Experimental results and comparisons prove that the 3SVM gives better outcomes and has a competitive performance relative to other published methods found in literature, where the average accuracy rate obtained is 98.75%.

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Section: A Results and Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis

Afif¹,

Hedar²,

Hamid³

et al. 2013

IJACSA

View full text Add to dashboard Cite

show abstract

“…As it is seen in this Table, classification process with EMELM and without feature reduction shows better results in comparison with other methods; the same as ELM. [12] 82.00 AIS Ster and dobnikar [16] 86.40 LDA Ster and dobnikar [16] 89.20 GAM De Bock et al [5] 91.25 PNN Bascil and oztekin [1] 91.87 MLP Bascil and temurtas [2] 93.75 ELM Kaya and Uyar [11] 93.75 EMELM Classifier [8] The learning method of EMELM has less complexity than ELM; as in ELM while applying the changes in the architecture of the network, the weights of previous outputs are ignored and new output weight is calculated using entirely new hidden layer output matrix. But in EMELM, as the network grows in each stage, hidden layer output matrix corresponding to number of nodes added is calculated and output weights are increasingly updated.…”

Section: Analysis and Experimental Resultsmentioning

confidence: 99%

“…The feature reduction using the PCA method is described in the following. For a given p-dimensional data set X, m principal axes T 1 , T 2 , … , T m where 1 ≤ m ≤ p, are orthonormal axes with maximum variance in projected space that can be given by the m leading eigenvectors of the sample covariance matrix Equation (1).…”

Section: Principal Component Analysis Algorithmmentioning

confidence: 99%

New method for rapid diagnosis of Hepatitis disease based on reduction feature and machine learning

Khorashadizade

Rezaei²

2015

JACST

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Hepatitis disease is caused by liver injury. Rapid diagnosis of this disease prevents its development and suffering to cirrhosis of the liver. Data mining is a new branch of science that helps physicians for proper decision making. In data mining using reduction feature and machine learning algorithms are useful for reducing the complexity of the problem and method of disease diagnosis, respectively. In this study, a new algorithm is proposed for hepatitis diagnosis according to Principal Component Analysis (PCA) and Error Minimized Extreme Learning Machine (EMELM). The algorithm includes two stages; in reduction feature phase, missing records were deleted and hepatitis dataset was normalized in [0,1] range. Thereafter, analysis of the principal component was applied for reduction feature. In classification phase, the reduced dataset is classified using EMELM. For evaluation of the algorithm, hepatitis disease dataset from UCI Machine Learning Repository (University of California) was selected. The features of this dataset reduced from 19 to 6 using PCA and the accuracy of the reduced dataset was obtained using EMELM. The results revealed that the proposed hybrid intelligent diagnosis system reached the higher classification accuracy and shorter time compared with other methods.

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“…Each hidden node models a Gaussian function centered on a training sample. In the summation layer, there is an output unit for each class, which is connected to all of the hidden units belonging to the same class, without connections to other neurons [4]. The outputs are proportional to the density functions of different classes and normalized to form a sum equal to 1.…”

Section: Pattern (Hidden) Layermentioning

confidence: 99%

A reduced probabilistic neural network for the classification of large databases

Lotfi¹,

Benyettou²

2014

Turk J Elec Eng & Comp Sci

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Abstract:The probabilistic neural network (PNN) is a special type of radial basis neural network used mainly for classification problems. Due to the size of the network after training, this type of network is usually used for problems with a small-sized training dataset. In this paper, a new training algorithm is presented for use with large training databases. Application to the handwritten digit database shows that the reduced PNN performs better than the standard PNN for all of the studied cases with a big gain in size and processing speed. This new type of neural network can be used easily for problems with large training databases like biometrics and data mining applications. An extension of the network is possible for new training samples and/or classes without retraining.

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A Study on Hepatitis Disease Diagnosis Using Probabilistic Neural Network

Cited by 44 publications

References 15 publications

SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis

SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis

New method for rapid diagnosis of Hepatitis disease based on reduction feature and machine learning

A reduced probabilistic neural network for the classification of large databases

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