Cancer Prediction Based on Radical Basis Function Neural Network with Particle Swarm Optimization

  • Yan, Xiao-Bo (College of Computer Science and Technology, Jilin University) ;
  • Xiong, Wei-Qing (School of Computer Science and Technology, Harbin Institute of Technology) ;
  • Hu, Liang (College of Computer Science and Technology, Jilin University) ;
  • Zhao, Kuo (College of Computer Science and Technology, Jilin University)
  • Published : 2014.10.11


This paper addresses cancer prediction based on radial basis function neural network optimized by particle swarm optimization. Today, cancer hazard to people is increasing, and it is often difficult to cure cancer. The occurrence of cancer can be predicted by the method of the computer so that people can take timely and effective measures to prevent the occurrence of cancer. In this paper, the occurrence of cancer is predicted by the means of Radial Basis Function Neural Network Optimized by Particle Swarm Optimization. The neural network parameters to be optimized include the weight vector between network hidden layer and output layer, and the threshold of output layer neurons. The experimental data were obtained from the Wisconsin breast cancer database. A total of 12 experiments were done by setting 12 different sets of experimental result reliability. The findings show that the method can improve the accuracy, reliability and stability of cancer prediction greatly and effectively.



  1. Al-Ammar AS, Barnes RM (2001). Supervised cluster classification using the original n-dimensional space without transformation into lower dimension. J Chemometrics, 15, 49-19.<49::AID-CEM631>3.0.CO;2-2
  2. Cheung MR (2013). Assessing the impact of socio-economic variables on breast cancer treatment outcome disparity. Asian Pac J Cancer Prev, 14, 7133-4.
  3. deGuzman MC, Prabhu N, Cramer N (2002). Automated breast cancer diagnosis based on fine needle aspiration. Analytical Quantitative Cytology Histology, 24, 305-9.
  4. Gao X, LaValley MP, Tucker KL (2005). Prospective studies of dairy product and calcium intakes and prostate cancer risk: a meta-analysis. J Nat Cancer Institute, 97, 1768-10.
  5. Golobardes E, Llora X, Salamo M, Marti J (2002). Computer aided diagnosis with case-based reasoning and genetic algorithms. Knowledge-Based Systems, 15, 45-8.
  6. Goodman VL, Brewer GL, Merajver SD (2004). Copper deficiency as an anti-cancer strategy. Endocrine-Related Cancer, 11, 255-9.
  7. Gupta SK, Singh SP, Shukla VK (2005). Copper, zinc, and Cu/Zn ratio in carcinoma of the gallbladder. J Surg Oncology, 91, 204-5.
  8. Hadjiiski LM, Chan HP, Sahiner B, et al (2004). Improvement of radiologists characterization of malignant and benign breast masses in serial mammograms by computer-aided diagnosis:an ROC study. Radiology, 233, 255-11.
  9. Hamilton PW, Anderson N, Bartels PH, Thompson D (1994). Expert system support using Bayesian belief networks in the diagnosis of fine needle aspiration biopsy specimens of the breast. J Clin Pathol, 47, 329-8.
  10. Hamilton PW, Anderson NH, Diamond J, et al (1996). An interactive decision support system for breast fine needle aspiration cytology. Anal Quantitative Cytology Histology, 18, 185-6.
  11. Hibi S, Funaki H, Ochiai-Kanai R, et al (1997). Hypercalcemia in children presenting with acute lymphoblastic leukemia. Int J Hematology, 66, 353-5.
  12. Karakitsos P, Ioakim-Liossi A, Pouliakis A, et al (1998). A comparative study of three variations of the learning vector quantizer in the discrimination of benign from malignant gastric cells. Cytopathology, 9, 114-2.
  13. Koksoy C, Kavas GO, Akcil E, et al (1997). Trace elements and superoxide dismutase in benign and malignant breast diseases. Breast Cancer Res Treat, 45, 1-6.
  14. Lee Y, Lee CK (2003). Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics, 19, 1132-8.
  15. Liu J, Li M (2005). Finding cancer biomarkers from mass spectrometry data by decision lists. J Computational Biology, 12, 971-9.
  16. Mayland C, Allen KR, Degg TJ, Bennett M (2004). Micronutrient concentrations in patients with malignant disease: effect of the inflammatory response. Annals Clin Biochemistry, 41, 138-4.
  17. Pena-Reyes CA, Sipper M (1999). A fuzzy-genetic approach to breast cancer diagnosis. Artif Intell Med, 17, 131-25.
  18. Peng SH, Xu QH, Ling XB, et al (2003). Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines. FEBS Letters, 555, 358-5.
  19. Polat K, Sahan S, Kodaz H, et al (2005). A new classification method for breast cancer diagnosis: feature selection artificial immune recognition system (FS-AIRS). Lecture Notes Computer Sci, 3611, 830-9.
  20. Rutkowska D, Klimala JK (2004). A multi-stage classification method in application to diagnosis of larynx cancer. LNAI, 3070, 1037-6.
  21. Sahiner B, Chan HP, Roubidoux MA, et al (2004). Computerized characterization of breast masses on 3-D ultrasound volumes. Med Phys, 31, 744-11.
  22. Uhl L, Maillet S, King S, Kruskall MS (1997). Unexpected citrate toxicity and severe hypocalcemia during apheresis. Transfusion, 37, 1063-3.
  23. Wang CY, Tsai T, Chen HM, Chen CT, Chiang CP (2003). PLSANN based classification model for oral submucous fibrosis and oral carcinogenesis. Lasers Surg Med, 32, 318-9.
  24. Xin J, Bie RF (2005). Classification Analysis of SAGE Data Using Maximum Entropy Model. LNAI, 3614, 1037-4.
  25. Zhang SC, Jin W, Liu H, et al (2013). RPSA gene mutants associated with risk of colorectal cancer among the Chinese population. Asian Pac J Cancer Prev, 14, 7127-5.
  26. Zhu J, Hastie T (2004). Classification of gene microarrays by penalized logistic regression. Biostatistics, 5, 427-17.