Comparison of artificial intelligence models for prognosis of breast cancer

Code

• github.com/anishsamantaray/Longitudinal-Analysis-of-Breast-Cancer
  Officialnone★ 3

Abstract

Breast cancer is the most common cancer not only affecting women but also men. Diagnosis and treatment are crucial stages in the cancer treatment process. However, even after treatment, individuals often experience ongoing challenges, including the regular need for painful procedures such as biopsies, MRIs, and scans as part of their journey towards recovery. We propose that in this case, machine learning (ML) and deep learning analyses may be used to perform longitudinal studies of women with breast cancer. We do a comparative analysis of three situations, in the first situation, we apply ML algorithms just after the primary preprocessing steps, in the second situation, we add balanced class weights hyperparameters, and in the third, we do principal component analysis (PCA). In the first situation, the light gradient boosting machine (LightGBM) gives the best accuracy of 87.87%, and the random forest (RF) gives an accuracy of 87.87% after the hyperparameter of balanced class weights is given. After PCA, logistic regression gives a maximum accuracy of 84.84%.

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Reproductions