publication . Article . 2017

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Cosma, G; Brown, D; Archer, M; Khan, M; Pockley, AG;
Open Access
  • Published: 01 Mar 2017 Journal: Expert Systems with Applications, volume 70, pages 1-19 (issn: 0957-4174, Copyright policy)
  • Publisher: Elsevier BV
  • Country: China (People's Republic of)
Abstract
Focus is on computational intelligence methods in prostate cancer predictive modeling.We survey metaheuristic optimisation methods.We review machine learning methods.We consider cancer data of different modalities.We discuss recent advances, challenges and provide future directions. Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced ...
Subjects
free text keywords: General Engineering, Artificial Intelligence, Computer Science Applications, Particle swarm optimization, Artificial neural network, Deep learning, Metaheuristic, Machine learning, computer.software_genre, computer, Data mining, Evolutionary computation, Computer science, Soft computing, Computational model, Computational intelligence, business.industry, business
82 references, page 1 of 6

Alexey, A. D., Eugenia, E., Rosenberg, G. S., & Krasnov, e. a. (2015). Identi cation of novel epigenetic markers of prostate cancer by noti-microarray analysis. Disease Markers, 2015 .

Arevalo, J. E., Cruz-Roa, A., Arias, V., Romero, E., & Gonzalez, F. A. (2015a). An unsupervised feature learning framework for basal cell carcinoma image analysis. Arti cial Intelligence in Medicine, 64 , 131{145.

1350 Arevalo, J. E., Gonzalez, F. A., Ramos-Pollan, R., Oliveira, J. L., & GuevaraLopez, M. A. (2015b). Convolutional neural networks for mammography mass lesion classi cation. In EMBC (pp. 797{800). IEEE.

Azizi, S., Imani, F., Zhuang, B., Tahmasebi, A., Kwak, J. T., Xu, S., Uniyal, N., Turkbey, B., Choyke, P., Pinto, P., Wood, B., Moradi, M., Mousavi, P., & Abolmaesumi, P. (2015). Medical image computing and computerassisted intervention { miccai 2015: 18th international conference, munich, germany, october 5-9, 2015, proceedings, part ii. chapter Ultrasound-Based Detection of Prostate Cancer Using Automatic Feature Selection with Deep Belief Networks. (pp. 70{77). Cham: Springer International Publishing.

1360 Balachandran, K., & Anitha, R. (2013). Ensemble based optimal classi cation model for pre-diagnosis of lung cancer. In Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on (pp. 1{7). IEEE.

Benecchi, L. (2006). Neuro-fuzzy system for prostate cancer diagnosis. Urology, 68 , 357 { 361. [OpenAIRE]

Bengio, Y. (2013). Statistical language and speech processing: First international conference, slsp 2013, tarragona, spain, july 29-31, 2013. proceedings. In A.-H. Dediu, C. Mart n-Vide, R. Mitkov, & B. Truthe (Eds.), Deep Learning of Representations: Looking Forward (pp. 1{37). Berlin, Heidelberg: Springer Berlin Heidelberg.

Bianchi, L., Dorigo, M., Gambardella, L., & Gutjahr, W. (2009). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8 , 239{287.

Black, W. C. (1999). Should this patient be screened for cancer? Clinical Practice, 2 , 86{95.

Bourdes, V., Bonnevay, S., Lisboa, P. J., Aung, M. H., Chabaud, S., Bachelot, T., Perol, D., & Negrier, S. (2007). Breast cancer predictions by neural networks analysis: a comparison with logistic regression. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE (pp. 5424{5427). IEEE. [OpenAIRE]

Castanho, M., Hernandes, F., De Re, A., Rautenberg, S., & Billis, A. (2013). Fuzzy expert system for predicting pathological stage of prostate cancer. Expert Systems with Applications , 40 , 466{470.

Cinar, M., Engin, M., Engin, E. Z., & Ziya Atesci, Y. (2009). Early prostate cancer diagnosis by using arti cial neural networks and support vector machines. Expert Syst. Appl., 36 , 6357{6361.

Chadha, K., Miller, A., Nair, B., Schwartz, S., Trump, D., & Underwood, W. (2014). New serum biomarkers for prostate cancer diagnosis. Clinical Cancer Investigation Journal , 3 , 72{79.

Choukroun, D., Bar-Itzhack, I. Y., & Oshman, Y. (2006). Novel quaternion kalman lter. IEEE Transactions on Aerospace and Electronic Systems , 42 , 174{190. [OpenAIRE]

Ciresan, D., Meier, U., & Schmidhuber, J. (2012). Multi-column deep neural networks for image classi cation. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) CVPR '12 (pp. 3642{ 3649). Washington, DC, USA: IEEE Computer Society. [OpenAIRE]

82 references, page 1 of 6
Abstract
Focus is on computational intelligence methods in prostate cancer predictive modeling.We survey metaheuristic optimisation methods.We review machine learning methods.We consider cancer data of different modalities.We discuss recent advances, challenges and provide future directions. Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced ...
Subjects
free text keywords: General Engineering, Artificial Intelligence, Computer Science Applications, Particle swarm optimization, Artificial neural network, Deep learning, Metaheuristic, Machine learning, computer.software_genre, computer, Data mining, Evolutionary computation, Computer science, Soft computing, Computational model, Computational intelligence, business.industry, business
82 references, page 1 of 6

Alexey, A. D., Eugenia, E., Rosenberg, G. S., & Krasnov, e. a. (2015). Identi cation of novel epigenetic markers of prostate cancer by noti-microarray analysis. Disease Markers, 2015 .

Arevalo, J. E., Cruz-Roa, A., Arias, V., Romero, E., & Gonzalez, F. A. (2015a). An unsupervised feature learning framework for basal cell carcinoma image analysis. Arti cial Intelligence in Medicine, 64 , 131{145.

1350 Arevalo, J. E., Gonzalez, F. A., Ramos-Pollan, R., Oliveira, J. L., & GuevaraLopez, M. A. (2015b). Convolutional neural networks for mammography mass lesion classi cation. In EMBC (pp. 797{800). IEEE.

Azizi, S., Imani, F., Zhuang, B., Tahmasebi, A., Kwak, J. T., Xu, S., Uniyal, N., Turkbey, B., Choyke, P., Pinto, P., Wood, B., Moradi, M., Mousavi, P., & Abolmaesumi, P. (2015). Medical image computing and computerassisted intervention { miccai 2015: 18th international conference, munich, germany, october 5-9, 2015, proceedings, part ii. chapter Ultrasound-Based Detection of Prostate Cancer Using Automatic Feature Selection with Deep Belief Networks. (pp. 70{77). Cham: Springer International Publishing.

1360 Balachandran, K., & Anitha, R. (2013). Ensemble based optimal classi cation model for pre-diagnosis of lung cancer. In Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on (pp. 1{7). IEEE.

Benecchi, L. (2006). Neuro-fuzzy system for prostate cancer diagnosis. Urology, 68 , 357 { 361. [OpenAIRE]

Bengio, Y. (2013). Statistical language and speech processing: First international conference, slsp 2013, tarragona, spain, july 29-31, 2013. proceedings. In A.-H. Dediu, C. Mart n-Vide, R. Mitkov, & B. Truthe (Eds.), Deep Learning of Representations: Looking Forward (pp. 1{37). Berlin, Heidelberg: Springer Berlin Heidelberg.

Bianchi, L., Dorigo, M., Gambardella, L., & Gutjahr, W. (2009). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8 , 239{287.

Black, W. C. (1999). Should this patient be screened for cancer? Clinical Practice, 2 , 86{95.

Bourdes, V., Bonnevay, S., Lisboa, P. J., Aung, M. H., Chabaud, S., Bachelot, T., Perol, D., & Negrier, S. (2007). Breast cancer predictions by neural networks analysis: a comparison with logistic regression. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE (pp. 5424{5427). IEEE. [OpenAIRE]

Castanho, M., Hernandes, F., De Re, A., Rautenberg, S., & Billis, A. (2013). Fuzzy expert system for predicting pathological stage of prostate cancer. Expert Systems with Applications , 40 , 466{470.

Cinar, M., Engin, M., Engin, E. Z., & Ziya Atesci, Y. (2009). Early prostate cancer diagnosis by using arti cial neural networks and support vector machines. Expert Syst. Appl., 36 , 6357{6361.

Chadha, K., Miller, A., Nair, B., Schwartz, S., Trump, D., & Underwood, W. (2014). New serum biomarkers for prostate cancer diagnosis. Clinical Cancer Investigation Journal , 3 , 72{79.

Choukroun, D., Bar-Itzhack, I. Y., & Oshman, Y. (2006). Novel quaternion kalman lter. IEEE Transactions on Aerospace and Electronic Systems , 42 , 174{190. [OpenAIRE]

Ciresan, D., Meier, U., & Schmidhuber, J. (2012). Multi-column deep neural networks for image classi cation. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) CVPR '12 (pp. 3642{ 3649). Washington, DC, USA: IEEE Computer Society. [OpenAIRE]

82 references, page 1 of 6
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publication . Article . 2017

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Cosma, G; Brown, D; Archer, M; Khan, M; Pockley, AG;