@article{(Open Science Index):https://publications.waset.org/pdf/10013111,
	  title     = {Breast Cancer Prediction Using Score-Level Fusion of Machine Learning and Deep Learning Models},
	  author    = {[email protected]},
	  country	= {},
	  institution	= {},
	  abstract     = {Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data need a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM), ensemble learning with hyper parameters optimization, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.},
	    journal   = {International Journal of Biotechnology and Bioengineering},
	  volume    = {17},
	  number    = {6},
	  year      = {2023},
	  pages     = {31 - 35},
	  ee        = {https://publications.waset.org/pdf/10013111},
	  url   	= {https://publications.waset.org/vol/198},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 198, 2023},
	}