Application of artificial neural networks in the diagnosis of urological dysfunctions

Gil, David; Johnsson, Magnus; García‐Chamizo, Juan Manuel; Payá, Antonio Soriano; Fernández, Daniel Ruíz

doi:10.1016/j.eswa.2008.06.065

Cited by 72 publications

(49 citation statements)

References 13 publications

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“…This combination has provided decision support with verified accuracy of almost 90%. Both, Faisal (Faisal et al, 2010) and Gil (Gil et al, 2009), also mentioned the possibility of increasing the accuracy of their systems by combining artificial neural networks with fuzzy inference techniques. This approach uses Kannappan (Kannappan et al, 2010) for design the system for prediction of autistic disorders using fuzzy cognitive maps with nonlinear Hebb learning algorithm.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Combining these techniques, they achieve 70% accuracy of forecasts. Gil et al (Gil et al, 2009) describe the use of artificial neural networks in the diagnosis of urological disorders. To suppress the main neural networks drawbacks, so called overlearning or over-fitting, they use a combination of three different ANN architectures, two unsupervised and one supervised.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

See 1 more Smart Citation

Design and Development of a Compound DSS for Laboratory Research

Hujer¹

2011

Efficient Decision Support Systems - Practice and Challenges From Current to Future

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Design and Development of a Compound DSS for Laboratory Research

Hujer¹

2011

Efficient Decision Support Systems - Practice and Challenges From Current to Future

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“…ANNs are applicable in many areas as well as in the decision and classification problems found in medical and biomedical fields, such as the diagnosis of various diseases (including hypertension, cancer, rheumatic diseases, and vertigo), the analysis of medical images obtained by MRI and X-rays, and the prediction of a history of a disease [9]. Similarly, an ANN is the frequently used method in the diagnosis of prostate cancer [10]- [16], detecting changes in tumor and cell structures [17]- [22], tracking periodic differences in retinal images [23] and various urological dysfunctions [16], [24]- [28].…”

Section: Related Workmentioning

confidence: 99%

Estimating the Semen Quality from Life Style Using Fuzzy Radial Basis Functions

Candemir¹

2018

IJMLC

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Abstract-Infertility is a major problem that directly affects many people worldwide. In recent years, fertility rates have decreased by up to 15% in young men. Changing living conditions, stress levels, environmental factors and nutritional habits play an important role in infertility. This paper suggests a novel method to estimate semen quality from lifestyle, environmental factors and daily habits using radial basis function neural networks. The accuracy of the suggested method was measured as 90%. The results show that the proposed method can be applied as a practical, fast and cheap alternative method to laboratory tests.

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“…The MLP network uses the back propagation algorithm [10], which is the most suitable algorithm for similar works [11,12].…”

Section: Ann Modelmentioning

confidence: 99%

Credit Risk of Bank Customers can be Predicted from Customer's Attribute using Neural Network

Saha¹,

Waheed²

2017

IJCA

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The aim of this paper is to present a model based on Multilayer perceptron neural networks to recognize bad or good credit customers. Credit risk is one of the major problems in banking sector. Banks are faced with credit Risk while doing their tasks. Credit risk is the probability of non-repayment of bank loan granted to lenders. Decreasing Credit Risk, banks may perform better duties and responsibilities successfully for the economic growth of the country. This study will help for a banker to select a right borrower for investing bank fund and hereby may reduce non-performing loan. Artificial neural network is used for loan applicants' credit risk measurement and the calculations have been done by using SPSS and WEKA software. Number of samples was 101 and 12 variables were used to identify good customers from bad customers. The results showed that, History of borrower (Defaulter or non-defaulter), amount of loan, type of collateral security (physical assets or financial assets) and Value of collateral security had most important effect in identifying classification criteria of good and bad borrowers. The main contribution of this paper is specifying for credit rating of bank customers in Bangladesh"s banking sector.

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Application of artificial neural networks in the diagnosis of urological dysfunctions

Cited by 72 publications

References 13 publications

Design and Development of a Compound DSS for Laboratory Research

Design and Development of a Compound DSS for Laboratory Research

Estimating the Semen Quality from Life Style Using Fuzzy Radial Basis Functions

Credit Risk of Bank Customers can be Predicted from Customer's Attribute using Neural Network

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