id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_d6kjnyqzwjcgzjuphidi4rvpvu	Kelwin Fernandes	Supervised deep learning embeddings for the prediction of cervical cancer diagnosis	2018	20	.pdf	application/pdf	8031	1046	52	supervised optimization of dimensionality reduction and classification models. with deep learning architectures, and achieved accurate prediction results (top area Autoencoder, Biomedical informatics, Binary classification, Deep learning, Cervical cancer, Identifying patients with the highest risk of developing cervical cancer can problem of predicting the patient's risk to develop cervical cancer through machine In this work, we propose a joint strategy to learn the low-dimensional space and the Generally, to tackle high-dimensional classification problems, machine learning this idea to model an individual patient's risk of having cervical cancer. The machine learning models we proposed achieved high prediction results, As shown in the Results section, our deep learning algorithm can predict cervical cancer Learning Repository: https://archive.ics.uci.edu/ml/datasets/Cervical+cancer+%28Risk https://archive.ics.uci.edu/ml/datasets/Cervical+cancer+%28Risk+Factors%29 https://archive.ics.uci.edu/ml/datasets/Cervical+cancer+%28Risk+Factors%29 https://github.com/kelwinfc/cervical-cancer-screening/tree/master/risk-factors/data https://github.com/kelwinfc/cervical-cancer-screening/tree/master/risk-factors/data Supervised deep learning embeddings for the prediction of cervical cancer diagnosis Supervised deep learning embeddings for the prediction of cervical cancer diagnosis Supervised deep learning embeddings for the prediction of cervical cancer diagnosis	./cache/work_d6kjnyqzwjcgzjuphidi4rvpvu.pdf	./txt/work_d6kjnyqzwjcgzjuphidi4rvpvu.txt