About: Deep Learning Strategies for Survival Prediction in Prophylactic Resection Patients

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Deep Learning Strategies for Survival Prediction in Prophylactic Resection Patients Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Deep Learning Strategies for Survival Prediction in Prophylactic Resection Patients
Creator	Chandra, Vinod Dhanya, S Hareendran, S Prasad, Sreedevi
Source	PMC
abstract	Human race is looking forward to an era where science and technology can wipeout the threats laid by lethal diseases. Major statistics shows that about 10 million people die from various forms of cancer annually. Every sixth death in the world is caused by cancer. Treatment to cancer always depend on its type and spread. Treatment includes single or combination of surgery, chemotherapy and radiation therapy. In this paper, survival prediction in prophylactic resection patients are carried out using various deep learning methods. Prophylactic resection has been found to be very effective in colon cancer, breast cancer and ovarian cancer. In this paper, we try to validate the results in a test environment using multi layered deep neural network. Classical Navie Bayer’s algorithm has been used to classify the dataset and convolution neural network (CNN) has been used to create the survival prediction model. Results affirm better survival results in prophylactic resection patients.
has issue date	2020-06-22(xsd:dateTime)
bibo:doi	10.1007/978-3-030-53956-6_53
has license	no-cc
sha1sum (hex)	f257933e4c890b45446f64662322f733ef1d0fae
schema:url	https://doi.org/10.1007/978-3-030-53956-6_53
resource representing a document's title	Deep Learning Strategies for Survival Prediction in Prophylactic Resection Patients
has PubMed Central identifier	PMC7354811
schema:publication	Advances in Swarm Intelligence
resource representing a document's body	covid:f257933e4c890b45446f64662322f733ef1d0fae#body_text
is schema:about of	named entity 'Prophylactic' named entity 'colon' named entity 'Bayer' named entity 'Results' named entity 'survival' named entity 'RADIATION THERAPY' named entity 'MAJOR' named entity 'CARRIED' named entity 'SPREAD' named entity 'EFFECTIVE' named entity '27S' named entity 'prediction' named entity 'caused' named entity 'affirm' named entity 'deep learning' named entity 'cancer' named entity 'die' named entity 'colon cancer' named entity 'prophylactic' named entity 'convolution' named entity 'Resection' named entity 'Deep Learning' named entity 'resection' named entity 'learning process' named entity 'Experimental results' named entity 'medical professional' named entity 'inductive logic' named entity 'classifiers' named entity 'Naïve Bayes' named entity 'resections' named entity 'classification model' named entity 'Neural networks' named entity 'Prophylactic' named entity 'test set' named entity 'Medical imaging' named entity 'machine learning' named entity 'SVM' named entity 'Prophylactic' named entity 'deep learning' named entity 'global optima' named entity 'reliable and accurate' named entity 'medical research' named entity 'breast cancer' named entity 'algorithm' named entity 'prophylactic' named entity 'SVM' named entity 'malignant or benign' named entity 'Naive Bayes classifier' named entity 'algorithm' named entity 't-test' named entity 'Deep neural networks' named entity 'Cox proportional hazards model' named entity 'Bayer' named entity 'feature extraction' named entity 'sensitivity, specificity' named entity 'prophylactic' named entity 'medical imaging' named entity 'naïve Bayes' named entity 'CNN' named entity 'ANN' named entity 'Experimental results' named entity 'data mining' named entity 'CNN' named entity 'prototype' named entity 'Lung cancer' named entity 'Root mean squared error' named entity 'ELM' named entity 'loss function' named entity 'lung cancer' named entity 'true negative' named entity 'clinical data' named entity 'Classifiers' named entity 'Survival rates' named entity 'feedback loops' named entity 'breast cancer' named entity 'survival analysis' named entity 'Bayes' named entity 'KNN' named entity 'state-of-the-art' named entity 'CNN'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software