10th International Congress on Information and Communication Technology

Authors - Dawngliani M S, Thangkhanhau H, Lalhruaitluanga
Abstract - Breast cancer continues to pose a major public health challenge world-wide, necessitating the development of accurate prediction algorithms to improve patient outcomes. This study aimed to devise a predictive model for breast cancer recurrence using machine learning techniques, with data sourced from the Mizoram State Cancer Institute. Utilizing the Weka machine learning toolkit, a hybrid approach incorporating classifiers such as K-Nearest Neighbors (KNN) and Random Forest was explored. Additionally, individual classifiers including J48, Naïve Bayes, Multilayer Perceptron, and SMO were employed to evaluate their predictive efficacy. Voting ensembles are utilized to augment performance accuracy. The hybridization of Random Forest and KNN classifiers, along with other base classifiers, demonstrated notable improvements in predictive performance across most classifiers. In particular, the combination of Random Forest with J48 yielded the highest performance accuracy at 82.807%. However, the J48 classifier alone achieved a superior accuracy rate of 84.2105%, signifying its efficacy in this context. Thus, drawing upon the analysis of the breast cancer dataset from the Mizoram State Cancer Institute, Aizawl, it was concluded that J48 exhibits the highest predictive accuracy compared to alternative classifiers.