Breast cancer risk prediction in African women using Random Forest Classifier.

Journal: Cancer treatment and research communications
Published Date:

Abstract

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.

Authors

  • Babafemi Oluropo Macaulay
    Department of Computer Science, Lagos State University, Nigeria.
  • Benjamin Segun Aribisala
    Department of Computer Science, Lagos State University, Nigeria. Electronic address: Benjamin.Aribisala@lasu.edu.ng.
  • Soji Alabi Akande
    Department of Surgery, Lagos State University Teaching Hospital, Nigeria.
  • Boluwaji Ade Akinnuwesi
    Department of Computer Science, Lagos State University, Nigeria.
  • Olusola Aanu Olabanjo
    Department of Computer Science, Lagos State University, Nigeria.