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Predictive Model for Likelihood of Survival among Breast Cancer Patients using Machine Learning Techniques

Olarinde Mobolaji, Ajinaja Micheal in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication:2020
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Olarinde Mobolaji, Ajinaja Micheal
10.5120/ijais2020451869
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  1. Olarinde Mobolaji and Ajinaja Micheal. Predictive Model for Likelihood of Survival among Breast Cancer Patients using Machine Learning Techniques. International Journal of Applied Information Systems 12(31):29-35, July 2020. URL, DOI BibTeX

    @article{10.5120/ijais2020451869,
    	author = "Olarinde Mobolaji and Ajinaja Micheal",
    	title = "Predictive Model for Likelihood of Survival among Breast Cancer Patients using Machine Learning Techniques",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "July 2020",
    	volume = 12,
    	number = 31,
    	month = "July",
    	year = 2020,
    	issn = "2249-0868",
    	pages = "29-35",
    	url = "http://www.ijais.org/archives/volume12/number31/1090-2020451869",
    	doi = "10.5120/ijais2020451869",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

Providing a prediction model that can give survival rate of breast cancer patients among women based on past records collected over the years in an underdeveloped country like Nigeria poses a challenge. This is because of their poor data collection habit and underdeveloped health care system. Machine learning (ML) offers a different approach and cheaper alternative of identifying survival rate among breast cancer patients among women. The purpose of this study is to provide survival rate or mortality rate of breast cancer patients after treatments has been administered. Naïve Bayes’ Machine learning techniques was used in developing a predictive model to predict survival rate of breast cancer patients among women. Data was gathered from 30 different health center location ranging from hospitals and institute. The data included all women who have been diagnosed with breast cancer from 2000 to 2005 and all death cases encountered so far. The simulation of the model was done using R Studio software. The result of the model was good as survival rate was above 85% showing incredible in the model used. Comparisons were made between some of the factors affecting breast cancer and survival rate using box plot. The results showed there is high survival rate in breast cancer patients among women in Nigeria. Other ML techniques can also be considered using same data to further improve the model.

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Keywords

Breast cancer, Survival, Nigeria, Predictive model, Naïve Baye, Machine learning