Cancer prediction using cascade generalization and duo output neural network

• Autorzy: Nilnumpetch Chatree , Amornsamankul Somkid , Kraipeerapun Pawalai

Identyfikatory

DOI

10.15439/2021R12

• Konferencja: Sixth International Conference on Research in Intelligent and Computing
• Języki publikacji: EN

Abstrakty

EN

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 machine. 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.

Słowa kluczowe

EN

cascade generalization; duo output neural network; feedforward neural network; binary classification; breast cancer; cervical cancer

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 27
• Strony: 65--70
• Opis fizyczny: Bibliogr. 19 poz., rys., tab., wykr.

Twórcy

autor

Nilnumpetch Chatree

• Department of Computer Science Ramkhamhaeng University, Bangkok, Thailand

autor

Amornsamankul Somkid

• Department of Mathematics Mahidol University and Centre of Excellence in Mathematics, Bangkok, Thailand

autor

Kraipeerapun Pawalai

• Department of Computer Science Ramkhamhaeng University, Bangkok, Thailand

Bibliografia

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• 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.
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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).