An Intelligent System for Predicting the Breast Cancer Threat Using Health Data Registry and Awareness: A Review

Tamil Selvi Madeswaran, Aruna Kumar Kavuru, Padma Theagarajan, Nasser Al Hadhrami, Maya Al Foori and Ohm Rambabu
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Tamil Selvi Madeswaran: University of Technology and Applied Sciences, Oman
Aruna Kumar Kavuru: University of Technology and Applied Sciences, Oman
Padma Theagarajan: Sona College of Technology, India
Nasser Al Hadhrami: University of Technology and Applied Sciences, Oman
Maya Al Foori: Royal Hospital, Oman
Ohm Rambabu: University of Technology and Applied Sciences, Oman

European Journal of Engineering and Technology Research, 2023, vol. 8, issue 3, 17-22

Abstract: Breast cancer is the most frequently diagnosed life-threatening cancer in women worldwide, with about 2.1 million new cases every year according to World Health Organization. Breast cancer represents about 34.1% of all reported cancer cases in Omani females, with an average age of 34.7 and high mortality rates of 11 per 100,000 populations (GLOBOCAN 2018). The main cause of breast cancer is changing lifestyle and the risk factors such as age, family history, early mensural age, late menopause, obesity and contraceptive pills. Observations of recent literature informed that the prevalence of breast cancer is due to combination of risk factors. Occasionally unknown risk factors will also be the cause for the occurrence of breast cancer. Also, the impact of contribution of each of the risk factors in the cancer occurrence varies among the females. The aim of this research is to review the supervised machine learning techniques specifically Logistic Regression, Neural Networks, Decision Trees and Nearest Neighbors in order to predict the possibility of occurrence of breast cancer among the female population.

Keywords: Artificial Intelligence; Breast Cancer; Machine Learning; Prediction; Regression; Risk Factors (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:8:y:2023:i:3:id:63012

DOI: 10.24018/ejeng.2023.8.3.3012

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