Logo PTI Logo rice

Proceedings of the 2021 Sixth International Conference on Research in Intelligent and Computing

Annals of Computer Science and Information Systems, Volume 27

Cancer Prediction Using Cascade Generalization and Duo Output Neural Network

, ,

DOI: http://dx.doi.org/10.15439/2021R12

Citation: Proceedings of the 2021 Sixth International Conference on Research in Intelligent and Computing, Vijender Kumar Solanki, Nguyen Ho Quang (eds). ACSIS, Vol. 27, pages 65–70 ()

Full text

Abstract. This paper proposes the combination of cascade generalization and duo output neural network based on feedforward backpropagation neural networks for cancer prediction. Duo output neural network is a neural network that is created based on two opposite targets in order to predict two opposite results. Cascade generalization is a technique that consists of a set of machines that are sorted together in which the predicted output produced from the previous machine plus the original training input are used for the creation of each ma-chine. In this study, cascade generalization is organized in two levels: the base level and the meta level. In this research, duo output neural network is trained in each level of cascade generalization. Two outputs produced from the base level which are truth output and non-falsity output are averaged. The average result plus the original input are used for training a machine in meta level. The proposed technique is tested using two cancer datasets from UCI machine learning repository and found that our technique provides the best overall results when compared with three individual techniques.

References

  1. T. E. Idriss, A. Idri, I. Abnane and Z. Bakkoury, "Predicting Blood Glucose using an LSTM Neural Network," 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), 2019, pp. 35-41.
  2. H. Benbrahim, H. Hachimi and A. Amine, “Deep Convolutional Neural Network with TensorFlow and Keras to Classify Skin Cancer Images,” Scalable Computing: Practice and Experience, vol. 21, no. 3, pp. 379-389, 2020.
  3. Y. Dai, B. Xu, S. Yan and J. Xu, “Study of cardiac arrhythmia classification based on Convolutional Neural Network,” Computer Science and Information Systems. vol. 17. no. 2, pp. 445-458, 2020.
  4. A. Birsen, I. A. Aydin, S. Rızalar, H. Oz, D. Meral, “Breast and Cervical Cancer Knowledge and Awareness among University Students,” Asian Pacific journal of cancer prevention: APJCP, vol. 16, pp. 1719-1724, 2015.
  5. R. Agrawal, “Predictive Analysis Of Breast Cancer Using Machine Learning Techniques”, ing. Solidar, vol. 15, no. 3, pp. 1-23, Sep. 2019.
  6. M. Patrício, J. Pereira, J. Crisóstomo, P. Matafome, M. Gomes, R. Seiça, F. Caramelo, “Using Resistin, glucose, age and BMI to predict the presence of breast cancer,” BMC Cancer, vol. 18, 2018.
  7. Sobar, R. Machmud, A. Wijaya, “Behavior Determinant Based Cervical Cancer Early Detection with Machine Learning Algorithm,” Advanced Science Letters, vol. 22, pp. 3120-3123, 2016.
  8. M. F. Aslan, Y. Celik, K. Sabanci, and A. Durdu, “Breast Cancer Diagnosis by Different Machine Learning Methods Using Blood Analysis Data”, IJISAE, vol. 6, no. 4, pp. 289-293, Dec. 2018.
  9. Y. Austria, M. Goh, L. Jr, J. Lalata, J. Goh, H. Vicente, “Comparison of Machine Learning Algorithms in Breast Cancer Prediction Using the Coimbra Dataset,” International journal of simulation: systems, science & technology, vol 20, 2019.
  10. G. Ullah, HaiYan, “Comparative performance analysis of machine learning models for breast cancer diagnosis,” International Journal of Scientific and Research Publications (IJSRP), vol. 10, no. 1, 2020.
  11. Naveen, R. K. Sharma, A. Ramachandran Nair, “Efficient Breast Cancer Prediction Using Ensemble Machine Learning Models,” In Proceedings of the 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), 2019, pp. 100-104.
  12. S. Poorani, P. Balasubramanie, “Deep Neural Network Classifier in Breast Cancer Prediction,” International Journal of Engineering and Advanced Technology (IJEAT), vol. 9, pp. 2106-2109, 2019.
  13. A. Karaci, “Predicting Breast Cancer with Deep Neural Networks,” In: Hemanth D., Kose U. (eds) Artificial Intelligence and Applied Mathematics in Engineering Problems. ICAIAME 2019. Lecture Notes on Data Engineering and Communications Technologies, vol. 43, pp. 996-1003, 2020.
  14. E., J. Kusuma, G. F. Shidik, R. A. Pramunendar, “Optimization of Neural Network using Nelder Mead in Breast Cancer Classification,” International Journal of Intelligent Engineering & Systems, vol. 13 , no. 6, pp. 330-337, 2020.
  15. K. Polat and U. Sentürk, "A Novel ML Approach to Prediction of Breast Cancer: Combining of mad normalization, KMC based feature weighting and AdaBoostM1 classifier," 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2018, pp. 1-4.
  16. E. Yavuz and C. Eyupoglu, “An effective approach for breast cancer diagnosis based on routine blood analysis features,” Medical & Biological Engineering & Computing, vol. 58, pp. 1583–1601, 2020.
  17. H. -J. Chiu, T. -H. S. Li and P. -H. Kuo, "Breast Cancer–Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine," in IEEE Access, vol. 8, pp. 204309-204324, 2020.
  18. N. F. Idris and M. A. Ismail, “Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition,” PeerJ Computer Science, vol. 7, e427, 2021.
  19. P. Kraipeerapun, S. Amornsamankul, C.C. Fung, S. Nakkrasae, “Applying Duo Output Neural Networks to Solve Single Output Regression Problem,” In: Leung C.S., Lee M., Chan J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol. 5863, pp. 554-561, 2009.