Educational interventions for primary healthcare professionals to promote the early diagnosis of cancer: a systematic review

2013 ◽  
Vol 24 (4) ◽  
pp. 274-290 ◽  
Author(s):  
Markus Schichtel ◽  
Peter W Rose ◽  
Charlotte Sellers
Keyword(s):  
Systematic Review ◽  
Early Diagnosis ◽  
Primary Healthcare ◽  
Healthcare Professionals ◽  
Educational Interventions ◽  
Early Diagnosis Of Cancer ◽  
Diagnosis Of Cancer

scholarly journals An investigation of efficient nursing interventions in early diagnosis of cancer: A systematic review and meta.analysis

2021 ◽  
Vol 10 (8) ◽  
pp. 2964
Author(s):  
Elnaz Saripour ◽  
NaghmehA Larimi ◽  
Iran Belash ◽  
Maede Abedi ◽  
Parisa Bandari ◽  
...  
Keyword(s):  
Systematic Review ◽  
Early Diagnosis ◽  
Nursing Interventions ◽  
Early Diagnosis Of Cancer ◽  
Diagnosis Of Cancer

scholarly journals Are community-based health worker interventions an effective approach for early diagnosis of cancer? A systematic review and meta-analysis

2017 ◽  
Vol 27 (4) ◽  
pp. 1089-1099 ◽  
Author(s):  
Sarah Bellhouse ◽  
Lorna McWilliams ◽  
Joseph Firth ◽  
Janelle Yorke ◽  
David P. French
Keyword(s):  
Systematic Review ◽  
Early Diagnosis ◽  
Health Worker ◽  
Meta Analysis ◽  
Community Based ◽  
Early Diagnosis Of Cancer ◽  
Diagnosis Of Cancer

scholarly journals On-Chip Liquid Biopsy: Progress in Isolation of Exosomes for Early Diagnosis of Cancer

2017 ◽  
Vol 112 (3) ◽  
pp. 461a ◽  
Author(s):  
Sung Cheol Kim ◽  
Navneet Dogra ◽  
Benjamin H. Wunsch ◽  
Joshua T. Smith ◽  
Stacey M. Gifford ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Liquid Biopsy ◽  
Early Diagnosis Of Cancer ◽  
Diagnosis Of Cancer ◽  
On Chip

A Survey on Using Nature Inspired Computing for Fatal Disease Diagnosis

2017 ◽  
Vol 8 (2) ◽  
pp. 70-91 ◽  
Author(s):  
Prableen Kaur ◽  
Manik Sharma
Keyword(s):  
Early Diagnosis ◽  
Disease Diagnosis ◽  
Fatal Disease ◽  
Swarm Optimization ◽  
Early Diagnosis Of Cancer ◽  
Nature Inspired Computing ◽  
Human Disorders ◽  
Diagnosis Of Cancer ◽  
Individual Use ◽  
Bee Colonies

Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bee Colonies (ABC) are some vital nature inspired computing (NIC) techniques. These approaches have been used in early prophecy of various diseases. This article analyzes the efficacy of various NIC techniques in diagnosing diverse critical human disorders. It is observed that GA, ACO, PSO and ABC have been successfully used in early diagnosis of different diseases. As compared to ACO, PSO and ABC algorithms, GA has been extensively used in diagnosis of ecology, cardiology and endocrinologist. In addition, from the last six years of research, it has been observed that the accuracy accomplished using GA, ACO, PSO and ABC in the early diagnosis of cancer, diabetes and cardio problems lies between 73.5%-99.7%, 70%-99.2%, 80%-98% and 76.4% to 99.98% respectively. Furthermore, ACO, PSO and ABC are found to be best suited in diagnosing lung, prostate and breast cancer respectively. Moreover, the hybrid use of NIC techniques produces better results as compared to their individual use.

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