An approach based on probabilistic neural network for diagnosis of Mesothelioma’s disease

Er, Orhan; Tanrıkulu, Abdullah Çetin; Abakay, Abdurrahman; Temurtaş, Feyzullah

doi:10.1016/j.compeleceng.2011.09.001

Cited by 66 publications

(51 citation statements)

References 29 publications

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“…In addition, it is considered as an implementation of the Bayes optimal decision rule in the NN form based on nearest neighbor classifiers [36,37]. Several recent studies [4,8,[38][39][40][41][42][43][44][45][46] used PNN for classification and showed that this method provides satisfactory results if the initial target classes are defined correctly. In this way, finding the basis function centers (classes) with their appropriate number is an important step to achieve suitable classification.…”

Section: Q4mentioning

confidence: 99%

Using an unsupervised approach of Probabilistic Neural Network (PNN) for land use classification from multitemporal satellite images

Iounousse

Er‐Raki

Motassadeq

et al. 2015

Applied Soft Computing

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Section: Q4mentioning

confidence: 99%

Using an unsupervised approach of Probabilistic Neural Network (PNN) for land use classification from multitemporal satellite images

Iounousse

Er‐Raki

Motassadeq

et al. 2015

Applied Soft Computing

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“…The use of ANN in classification is considered very important in research and application areas [24]. One of the most common neural networks architecture that has been used for diseases prediction and diagnosis systems is the multi-layer neural networks architecture [25]. Additionally, a study by [26] used a deep convolutional neural networks (DCNN) to classify several types of cancer disease.…”

Section: Literature Reviewsmentioning

confidence: 99%

Neural Network-based Diabetic Type II High-Risk Prediction using Photoplethysmogram Waveform Analysis

Qawqzeh¹

2019

IJACSA

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This work aims to predict and classify patients into diabetic and nondiabetic subjects based on age and four independent variables extracted from the analysis of photoplethysmogram (PPG) morphology in time domain. The study has two main stages, the first one was the analysis of PPG waveform to extract b/a, RI, DiP, and SPt indices. These parameters contribute by some means to the prediction of diabetes. They were statistically significant and correlated with the HbA1C test. The second stage was building a neural network based classifier to predict diabetes. The model showed an accuracy of 90.2% in training phase and an accuracy of 85.5% in testing phase. The findings of this research work may contribute towards the prediction of diabetes in early stages. Also, the proposed classifier showed a high accuracy in predicting the existence of diabetes in Saudi people population.

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“…There is also a variable for each record that represents the diagnostic class. Of 324 records, 228 were identified as healthy and 96 as patients [26]. ANN classification is implemented by Alyuda NeuroIntelligence 2.2. whereas others were implemented by WEKA (The University of Waikato).…”

Section: Methodsmentioning

confidence: 99%

Diagnosis of Mesothelioma Disease Using Different Classification Techniques

Tutuncu

Cataltas

2017

ijisae

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Mesothelioma, which is a disease of the pleura and peritoneum, is an asbestos-related environmental disease in undeveloped countries. Although the incidence of this disease is lower than that of lung cancer, the reaction it creates in society is very high. In this study, 9 different classification algorithms of data mining were applied to the Mesethelioma data set obtained from real patients in Dicle University, Faculty of Medicine and loaded into UCI Machine Learning Repository, and the results were compared. When the obtained results were examined, it has been seen that Artificial Neural Network (ANN) had %99.0740 correct classification ratio.

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An approach based on probabilistic neural network for diagnosis of Mesothelioma’s disease

Cited by 66 publications

References 29 publications

Using an unsupervised approach of Probabilistic Neural Network (PNN) for land use classification from multitemporal satellite images

Using an unsupervised approach of Probabilistic Neural Network (PNN) for land use classification from multitemporal satellite images

Neural Network-based Diabetic Type II High-Risk Prediction using Photoplethysmogram Waveform Analysis

Diagnosis of Mesothelioma Disease Using Different Classification Techniques

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