Self-Adaptive Artificial Neural Networks Applied to Brain Death Level and Antibiotics Treatment of Gastric Lymphoma Cancer Prognostication

• Main Authors: Muammar Sadrawi, 莫哈馬
• Other Authors: Jiann-Shing Shieh
• Format: Others
• Language: en_US
• Online Access: http://ndltd.ncl.edu.tw/handle/20512668241184827337

碩士 === 元智大學 === 機械工程學系 === 101 === These studies evaluated the applications of Artificial Neural Networks (ANNs) for being used in brain death patient level and gastric lymphoma cancer antibiotics. Back-propagation neural network (BPNN) has been applied to make the prediction beside the fuzzy model for brain death patient level. The model is influenced by 12 inputs. The results showed the self-adaptive ensembled neural networks (SeA-EANN) gave the best model followed by manually tuned fuzzy modeling, ensembled neuro fuzzy inference system (EANFIS) and ensembled neural networks (EANN), which produced testing MSE 0.00845, 0.019, 0.021 and 0.026, respectively. Another case, using single SeA-ANN, as the model for distinguishing resistive and sensitive patient to antibiotics also has been developed. We used multiple sensitivity analysis in choosing the most important genes, from 654 to 50. For the result, we got 90% accuracy and several genes can be used for further clinical validation.