Predicting environment effects on breast cancer by implementing machine
  learning
Muhammad Shoaib Farooq Mehreen Ilyas
The biggest Breast cancer is increasingly a major factor in female
fatalities, overtaking heart disease. While genetic factors are important in
the growth of breast cancer, new research indicates that environmental factors
also play a substantial role in its occurrence and progression. The literature
on the various environmental factors that may affect breast cancer risk,
incidence, and outcomes is thoroughly reviewed in this study report. The study
starts by looking at how lifestyle decisions, such as eating habits, exercise
routines, and alcohol consumption, may affect hormonal imbalances and
inflammation, two important factors driving the development of breast cancer.
Additionally, it explores the part played by environmental contaminants such
pesticides, endocrine-disrupting chemicals (EDCs), and industrial emissions,
all of which have been linked to a higher risk of developing breast cancer due
to their interference with hormone signaling and DNA damage. Algorithms for
machine learning are used to express predictions. Logistic Regression, Random
Forest, KNN Algorithm, SVC and extra tree classifier. Metrics including the
confusion matrix correlation coefficient, F1-score, Precision, Recall, and ROC
curve were used to evaluate the models. The best accuracy among all the
classifiers is Random Forest with 0.91% accuracy and ROC curve 0.901% of
Logistic Regression. The accuracy of the multiple algorithms for machine
learning utilized in this research was good, which is important and indicates
that these techniques could serve as replacement forecasting techniques in
breast cancer survival analysis, notably in the Asia region.