title: A hybrid particle swarm and neural network approach for detection of prostate cancer from benign hyperplasia of prostate.
abstract: In present paper, we propose a Hybrid classifier based particle swarm optimization (PSO) and Neural Network method for supporting the diagnosis of prostate cancer. algorithm combining particle swarm optimization algorithm with back propagation neural network (BPNN) algorithm, also referred to as BPNN-PSO algorithm, is proposed to train the feed forward neural network (FNN). The results show that the proposed BP based PSO algorithm can achieve very high diagnosis accuracy (98%) and it proving its usefulness in support of clinical decision process of prostate cancer.
authors.affiliation: School of Health Management & Information Sciences, Iran University of Medical Sciences.
authors.forename: Farhnaz
authors.initials: F
authors.lastname: Sadoughi
authors.affiliation: Researcher in Medical Health informatics, Iran University of Medical Siences, Iran.
authors.forename: Mustafa
authors.initials: M
authors.lastname: Ghaderzadeh
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011470:Prostatic Hyperplasia / Q000175:diagnosis*; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity