Breast Cancer Prediction using Optimized Machine Learning Classifiers and Data Balancing Techniques
Saniya Anklesaria, Unnati Maheshwari, Ria Lele, Priyanka Verma · 2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA · 2022
The most prevalent malignancy in females and the second greatest reason for the loss of life from cancer is Breast Cancer. Hence, a computer assisted detection (CAD) system that uses a machine learning technique to give reliable breast cancer diagnosis is required. The paper is aimed to incorporate several machine learning (ML) algorithms, including Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbour (KNN), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and Nave Bayes (NB) with hyperparameter tuning using the Random Forest Feature Importance Method for feature selection. These models have been trained on the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset. Furthermore, the dataset was balanced using both Undersampling and SMOTE from which we concluded that Undersampling gave us an overall better result. The performance evaluation parameters for the designed model are specificity, accuracy, sensitivity, F1 score, precision, recall and Area Under the Curve(AUC). The research concluded that the Support Vector Machine Algorithm proved to be the most effective model which fit our dataset with an Accuracy of 95.8% followed by KNN with an accuracy of 95.3%.