Genetic algorithm pruning of probabilistic neural networks in medical disease estimation

Mantzaris, Dimitrios; Anastassopoulos, George; Adamopoulos, Adam

doi:10.1016/j.neunet.2011.06.003

Cited by 82 publications

(45 citation statements)

References 12 publications

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“…PNN is frequently utilized in many applications, e.g. : medical diagnosis and prediction [18], [19], [20], [21], image classification and recognition [22], [23], [24], multiple partial discharge sources classification [25], interval information processing [26], [27], phoneme recognition [28], email security enhancement [29], intrusion detection systems [30] or classification in a time-varying environment [31].…”

Section: Probabilistic Neural Networkmentioning

confidence: 99%

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Determining the significance of features with the use of Sobol' method in probabilistic neural network classification tasks

Kowalski¹,

Kusy²

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-In this article, the problem of determining the significance of data features is considered. For this purpose the algorithm is proposed, which with the use of Sobol method, provides the global sensitivity indices. On the basis of these indices, the aggregated sensitivity coefficients are determined which are used to indicate significant features. Using such an information, the process of features' removal is performed. The results are verified by the probabilistic neural network in the classification of medical data sets by computing model's quality. We show that it is possible to point the least significant features which can be removed from the input space achieving higher classification performance.

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Section: Probabilistic Neural Networkmentioning

confidence: 99%

“…whereσ denotes the estimator of the standard deviation and for the Cauchy kernel in (19), R(K) = 1 and U (K) = 5/4. The calculation ofσ is solved iteratively using second-order level approximation [34], [33].…”

Section: B Training Algorithmmentioning

confidence: 99%

Determining the significance of features with the use of Sobol' method in probabilistic neural network classification tasks

Kowalski¹,

Kusy²

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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“…Probabilistic neural networks have found their implementation in a variety of classification fields. It was presented in image classification and recognition (Chtioui et al 1996(Chtioui et al , 1998Ramakrishnan and Selvan 2007;Wen et al 2008), earthquake magnitude prediction (Adeli and Panakkat 2009), multiple partial discharge sources classification (Venkatesh and Gopal 2011), interval information processing (Kowalski and Kulczycki 2014) or medical diagnosis and prediction (Shan et al 2002;Folland et al 2004;Huang and Liao 2004;Temurtas et al 2009;Mantzaris et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Assessment of prediction ability for reduced probabilistic neural network in data classification problems

Kusy

Kluska

2016

Soft Comput

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One of the most important problems in probabilistic neural network (PNN) operation is the minimization of its structure. In this paper, two heuristic approaches of PNN's pattern layer reduction are applied. The first method is based on a k-means clustering procedure. In the second approach, the candidates for the network's pattern neurons are selected on the basis of a support vector machines algorithm. Modified models are compared in the classification problems with the traditional PNN, four well-known computational intelligence algorithms (single decision tree, multilayer perceptron, support vector machines, k-means algorithm) and PNN trained by the state-of-the-art procedures. Seven medical benchmark databases are investigated and one authors' own real ovarian cancer data set. Comparison is performed on the basis of the global performance indices which depend on the accuracy, sensitivity and specificity. These indices are computed using the standard tenfold cross-validation procedure. On the basis of the reported results, we show that the algorithm based on k-means clustering is a better PNN structure reduction procedure. Furthermore, this algorithm is much less timeconsuming.

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“…In addition, they can also eliminate noise and reduce features to achieve a classification with higher accuracy. Examples of common classification methods include Naïve Bayes (NB) [6,7,8,9], Support Vector Machine (SVM) [10,11,12,13], Genetic Algorithm (GA) [14,15,16], k-Nearest Neighbor (KNN) [17] and Multilayer Perceptron (MLP) [18]. These efficient methods are able to aid doctors in making decision of diagnosis based on the features obtained from the classification.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Oral Cancer Prediction using Features Selection with Machine Learning

Mohd

Maizura

Noor

et al. 2015

The 7th International Conference on Information Technology

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Abstract-Accuracy is one of the main elements in the disease diagnose. Thus, it is important to select most relevant attributes to generate the optimal accuracy. The objective of this study is to predict more accurately the presence of oral cancer primary stage with reduced number of attributes. Originally, 25 attributes have been identified in order to predict the oral cancer staging. In this study, the integrated diagnostic model with hybrid features selection methods is used to determine the attributes that contribute the most to the diagnosis of oral cancer, which, indirectly, reduces the number of features that are collected from a variety of patient records. Twentyfive attributes have been reduced to 14 features using hybrid feature selection. Subsequently, four classifiers: Updatable Naïve Bayes, Multilayer Perceptron, K-Nearest Neighbors and Support Vector Machine are used to predict the diagnosis of patients with oral cancer. Also, the observations indicate that the Support Vector Machine outperforms other machine learning algorithms after incorporating feature subset selection with SMOTE at preprocessing phases.

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Genetic algorithm pruning of probabilistic neural networks in medical disease estimation

Cited by 82 publications

References 12 publications

Determining the significance of features with the use of Sobol' method in probabilistic neural network classification tasks

Determining the significance of features with the use of Sobol' method in probabilistic neural network classification tasks

Assessment of prediction ability for reduced probabilistic neural network in data classification problems

Analysis of Oral Cancer Prediction using Features Selection with Machine Learning

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