Breast Cancer Statistics and Prediction Methodology: A Systematic Review and Analysis

HESC-500 Statistic Applied

Keywords

Breast cancer

Incidence and mortality

Prevalence

Data mining

Evolutionary algorithms

INTRODUCTION

Breast cancer is a menacing cancer, primarily affecting women. Continuous research is going on for detecting breast cancer in the early stage as the possibility of cure in early stages is bright.

Current stated that the breast cancer is most frequently found cancer in the women and it is adversary affecting millions of women all over the world.

But the positive trend is that the death rate is gradually declining after 1990 due to screening, early detection, awareness and continuous improvement in treatment. But the death rate of patients of breast cancer is still very high.

OBJECTIVE

The first objective of this paper is to make survey and analysis of breast cancer incidence and mortality of different countries to find out the survival and death rates.

The second objective is to bring to light the advantages and disadvantages of those algorithms and methods which can help in predicting breast cancer at the initial stage. This step will be helpful in the development of a new framework which will prove to be a milestone for cancer detection at early stage. As in case breast cancer is detected in early stage the chances of curing will be bright.

Methodology of the investigation

The following sources are used for data collection of incidence and mortality rates from different countries: National Cancer Intelligence Network (NCIN), UK: NCIN coordinate to develop analysis for improving clinical outcomes, cancer care and prevention. It is a part of Public Health England from April 2013.

Office for National Statistics (ONS), UK: ONS is a recognized statistical institute of the UK. It collects and publishes statistics related to population, society and economics.

Northern Ireland Cancer Registry (N. Ireland Cancer Registry): It was established in 1994, which is located in Centre for Public Health, Queen’s University Belfast. It register and maintain cancer incidence and mortality. This agency is funded by the Public Health Agency for Northern Ireland.

DATA ANALYSIS

The incidence and mortality rates Females was calculated based on new cancer cases found in the European age-standardized rates per 100,000 populations in the UK. The incidence rates were increased in the period of 1975-2010. I 0. That the rates of mortality were gone high in 1975-1985, but it is gone down from 1990-2014 because of several reasons like better screening, therapies and medical care. The female death rate(2010-2012) in the UK at the age of 50-69 is 34 %. It means the risk is increased at the higher age in comparison to the younger age.

Evolutionary Algorithms have been discussed and analysed. Evolutionary algorithms are used to find the nearer or optimal solution even in the case of complex problems.

The epidemiology results are not based on random assumptions; instead these are based on experiments, predictable patterns and observations,

RESULTS

The finding of this study proved that the overall mortality rates of the UK and US have been improved because of awareness, improved medical technology and screening, but in case of India and Egypt the condition is less positive because of lack of awareness.

The methodological findings of this study suggest a combined framework based on data mining and evolutionary algorithms.

A better understanding of the current health situation.

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Conclusions and Scientific Significance.

This study has been made on methodologies by which the breast cancer can be detected at early stages by using the breast cancer data set.

The characteristics of breast cancer symptoms are different, so the chances of good results by using single algorithm are less. But by the use of combined algorithms at different levels will produce good results.

So it is concluded that the framework based on data mining and evolutionary algorithms can be a milestone in case of breast cancer detection.

REFERENCES

https://www.ncbi.nlm.nih.gov/pubmed/26028079 Breast Cancer Statistics and Prediction Methodology: A Systematic Review and Analysis Ashutosh Kumar Dubey*, Umesh Gupta, Sonal Jain

Dheeba J, Selvi ST (2011). A CAD system for breast cancer diagnosis using modified genetic algorithm optimized artificial neural network. In Swarm, Evolutionary, and Memetic Computing.

Lee KE, Sha N, Dougherty ER, et al (2003). Gene selection: a Bayesian variable selection approach. Bioinformatics.