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Epidemiology of lung cancer and approaches for its prediction: a systematic review and analysis

Overview of attention for article published in Cancer Communications, July 2016
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Title
Epidemiology of lung cancer and approaches for its prediction: a systematic review and analysis
Published in
Cancer Communications, July 2016
DOI 10.1186/s40880-016-0135-x
Pubmed ID
Authors

Ashutosh Kumar Dubey, Umesh Gupta, Sonal Jain

Abstract

Owing to the use of tobacco and the consumption of alcohol and adulterated food, worldwide cancer incidence is increasing at an alarming and frightening rate. Since the last decade of the twentieth century, lung cancer has been the most common cancer type. This study aimed to determine the global status of lung cancer and to evaluate the use of computational methods in the early detection of lung cancer. We used lung cancer data from the United Kingdom (UK), the United States (US), India, and Egypt. For statistical analysis, we used incidence and mortality as well as survival rates to better understand the critical state of lung cancer. In the UK and the US, we found a significant decrease in lung cancer mortalities in the period of 1990-2014, whereas, in India and Egypt, such a decrease was not much promising. Additionally, we observed that, in the UK and the US, the survival rates of women with lung cancer were higher than those of men. We observed that the data mining and evolutionary algorithms were efficient in lung cancer detection. Our findings provide an inclusive understanding of the incidences, mortalities, and survival rates of lung cancer in the UK, the US, India, and Egypt. The combined use of data mining and evolutionary algorithm can be efficient in lung cancer detection.

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Mendeley readers

The data shown below were compiled from readership statistics for 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ecuador 1 <1%
Unknown 138 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 18 13%
Student > Master 17 12%
Student > Ph. D. Student 9 6%
Other 8 6%
Student > Postgraduate 8 6%
Other 24 17%
Unknown 55 40%
Readers by discipline Count As %
Medicine and Dentistry 38 27%
Computer Science 9 6%
Nursing and Health Professions 8 6%
Biochemistry, Genetics and Molecular Biology 7 5%
Engineering 5 4%
Other 12 9%
Unknown 60 43%