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Breast cancer risk prediction in African women using Random Forest Classifier by Macaulay, Babafemi Oluropo (author);Aribisala, Benjamin Segun (author);Akande, Soji Alabi (author);Akinnuwesi, Boluwaji Ade (author);Olabanjo, Olusola Aanu (author) is a scholarly article available to read on EtoBox.

What is Breast cancer risk prediction in African women using Random Forest Classifier about?

## Introduction: One of the most important steps in combating breast cancer is early and accurate diagnosis. unfortunately, breast cancer is asymptomatic at the early stage, although some symptoms are presented at a later time, but at symptomatic stage treatment could be complicated or even become impossible thereby leading to death. proper risk assessment is hence very important in reducing mortality. some computational techniques have been developed for breast cancer risk assessment in the developed world, but such techniques do not work well in africa because of the difference in risk profiles of african women e.g. later menarche, low drug abuse and low smoking rate. ## Aim: In this work, we propose a bespoke risk prediction model for african women using random forest classifier (rfc) machine learning technique. ## Methods: A total of 180 subjects were studied out of which 90 were confirmed cases of breast cancer and 90 were benign. twenty-five risk factors were included, for example, smoking, alcohol intake, occupational hazards and age at menopause. four approaches were empirically used in the feature selection, these are the use of chi-square, mutual information gain, spearma

Author

Macaulay, Babafemi Oluropo (author);Aribisala, Benjamin Segun (author);Akande, Soji Alabi (author);Akinnuwesi, Boluwaji Ade (author);Olabanjo, Olusola Aanu (author)

Publisher

Elsevier BV

Published

2021

Language

EN