Tajelsir M Ali, Eman A Keshk, Osama M Almaqadi, Khader M Alsawlihah, Mohammed M Alzahrani, Ahmed A Alzahrani, Abdullah Y Alsalhi, Saeed M Alzahrani, Jamaan A Alzahrani, Muteb A Alzahrani
Awareness of Gestational Diabetes Mellitus Among Women in the Al-Baha Region, Saudi Arabia
Cureus
breast cancer prevalent type cancer women risk factor assessment aid directing counseling regarding risk reduction breast cancer surveillance research aims 1 investigate relationship various risk factors breast cancer incidence using bcsc breast cancer surveillance consortium risk factor dataset create prediction model assessing risk developing breast cancer 2 diagnose breast cancer using breast cancer wisconsin diagnostic dataset 3 analyze breast cancer survivability using seer surveillance epidemiology end results breast cancer dataset applying resampling techniques training dataset using various machine learning techniques affect performance classifiers three breast cancer datasets examined using variety preprocessing approaches classification models assess performance terms accuracy precision f1 scores etc pca principal component analysis resampling strategies produced remarkable results bcsc dataset random forest algorithm exhibited best performance applied classifiers accuracy 8753 different resampling techniques applied training dataset training random forest classifier tomek link exhibited best test accuracy 8747 compared models used previously used techniques applying resampling techniques accuracy scores test data decreased even training data accuracy increased breast cancer wisconsin diagnostic dataset knearest neighbor algorithm best accuracy original dataset test set 9471 pca dataset test set exhibited 9529 accuracy detecting breast cancer using seer dataset study also explores survival analysis employing supervised unsupervised learning approaches offer insights variables affecting breast cancer survivability study emphasizes significance individualized approaches management treatment breast cancer incorporating phenotypic variations recognizing heterogeneity disease datadriven insights advanced machine learning study contributes significantly ongoing efforts breast cancer research diagnostics personalized medicine