Abstract:
Many efforts have been devoted to developing efficient methods for early detection and
prediction in order to address this urgent problem. My suggested strategy makes use of
risk prediction algorithms and encourages recent breast cancer awareness. My strategy
offers a simple way to forecast breast cancer and stands out for its practical usefulness
in real-world circumstances. Using dataset provided on the UCI platform, I included a
variety of classifiers, such as “K-Nearest Classifier (KNN), Random Forest (RF),
Decision Tree (DT), Support Vector Machine (SVM), Naïve Bayes, and Logistic
Regression (LR)”. The findings were encouraging. Moreover, Boosting Decision Tree
showed accuracy that met this exacting requirement. I applied a variety of techniques,
including as Bagging, Boosting, and Voting, to further improve performance. In
addition to adding to the corpus of information on breast cancer detection and
prediction, my experimental analysis revealed the most accurate model to be the
Logistic Regression model, which achieved an exceptional accuracy rate of 97.37% for
breast cancer. The main aim of this thesis patient outcomes enhances by presenting a
viable early intervention approach and important new insights into the management.
