Breast Cancer Management System Using Decision Tree and Neural Network

Abstract

Nowadays, people are facing various health-related problems due to the modern life style what they follow. Breast Cancer is one of the most common problems among women worldwide which affects approximately 2.1 million women each year. Hence, it has become paramount to develop a system that can identify the major risk factors of Breast Cancer beforehand to make women aware about the risk factors and to take some precautionary measures to manage Breast Cancer. Consequently, this paper proposes a system called Transparent Breast Cancer Management System using P-Rules (TBCMS-PR) which identifies the major risk factors responsible for Breast Cancer in detail using decision tree and neural Network. TBCMS-PR uses decision tree to generate the rules for deciding the decision of Breast Cancer. Neural Network is used to keep only the relevant rules for Breast Cancer and to drop the irrelevant ones. Finally, the major risk factors with ranges are identified based on Sequential Search algorithm. The performance of the TBCMS-PR system is validated with the Breast Cancer dataset collected from UCI repository and is compared with a recent existing system. From the experimental results, it is observed that the proposed TBCMS-PR is significant and potential to manage Breast Cancer to a great extent by managing only one or two major risk factor(s).

[Retracted] Comparative Analysis of Machine Learning Methods for Breast Cancer Classification in Genetic Sequences

“…Gradient boosting has achieved better accuracy than the decision tree technique. Transparent breast cancer management is developed for identifying major risk components in the occurrence of BC with the decision tree as well as the neural network [4].…”

Advanced feature learning and classification of microscopic breast abnormalities using a robust deep transfer learning technique

“…The BACH dataset faces a significant limitation due to its small number of breast cancer histological images, which proves insufficient for effectively training high-performance CNN models that typically require a larger dataset. To tackle this issue, Weiss et al (2018) researched the interactive classification of breast cancer histopathological images, utilizing Xception (Malve & Vijay, 2023) and incorporating a logistic regression (LR) classifier. They applied data augmentation techniques, such as horizontal and vertical rotating, to achieve an accuracy of 72% on the BACH hidden test set.…”