An Intelligent System Based on Back Propagation Neural Network and Particle Swarm Optimization for Detection of Prostate Cancer from Benign Hyperplasia of ProstateFarahnaz Sadoughi1, Mustafa Ghaderzadeh2*, Mohsen Solimany3 and Rebecca Fein4
- *Corresponding Author:
- Mustafa Ghaderzadeh
Researcher in Medical Health informatics
Tehran University of Medical Sciences, Iran
E-mail: [email protected]
Received date: April 17, 2014; Accepted date: July 22, 2014; Published date: July 28, 2014
Citation: Sadoughi F, Ghaderzadeh M, Solimany M, Fein R (2014) An Intelligent System Based on Back Propagation Neural Network and Particle Swarm Optimization for Detection of Prostate Cancer from Benign Hyperplasia of Prostate. J Health Med Informat 5:158. doi:10.4172/2157-7420.1000158
Copyright: © 2014 Sadoughi F, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Conventional clinical diagnostic methods are generally based on a single classifier. In present paper, we propose a hybrid Backpropagation neural network (BPNN) classifier based particle swarm optimization (PSO) method. In the present paper by combining the principles two algorithm, we propose a new but simple hybrid algorithm called BPNN_ PSO. Our novel algorithm optimizes BPNN with PSO and reduces computational time of the training phase of BPNN. The performance of the algorithm has been tested with prostate cancer. A total of 360 medical records collected from the patients suffering from neoplasia diseases have been used to train and test the proposed algorithm. The results show that the proposed BPNN–PSO algorithm can achieve very high diagnosis accuracy (98%) and it proving its usefulness in supporting of clinical decision process of prostate cancer. Comparing the simulated results of the above two cases, training the neural network by PSO technique gives more accurate (in terms of sum square error) and also faster (in terms of number of iterations and simulation time) results than BPNN. By using these hybrid method for building machine learning classifiers, we can significantly improve diagnostic performance with respect to the results of clinical practice.