Identifying The Best-Fit Associative Classifier For Determining Survivability And Non-Survivability In Breast Cancer Patients - Sheethal Aji Mani (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In developing nations, cancer death is considered one of the biggest challenges. Even though there are various strategies to prevent cancer, some types of cancer still receive inadequate treatment. One of these is breast cancer. Early diagnosis is very important in treating this disease. Breast cancer has a good survival rate, especially when it is detected early. This can be attributed to better treatment and early diagnosis. This study aims to classify the various medical attributes collected from the SEER dataset for breast cancer patients into two categories: non-survivability and survivability. The paper used associative classifiers such as ACAC, ACN, L3, and CBA2 to analyze the data. The objective of the study is to identify a best fit classifier for deciding survivability and non-survivability in breast cancer patients based on accuracy. The outcome of the study revealed that CBA and CBA2 model exhibited an accuracy of 81% even in a huge dataset with 44325 records. The proposed approach also showed an improvement in performance. These findings indicated that the possibility of identifying non-survivability and survivability in breast cancer patients could be explored