WCBA: Weighted classification based on association rules algorithm for breast cancer disease

Breast cancer is the second most frequent human neoplasm that accounts for one quarter of all cancers in females. Among the other types of cancers, it is considered to be the main cause of death in women in most countries. An efficient classifier for accurately helping physicians to predict this chronic disease is in high demand. One approach for solving this problem has been tackled by many scholars using Association Classification (AC) techniques to enhance the classification process through applying association rules. However, most AC algorithms are suffering from the estimated measures used in the rule evaluation process and the prioritization techniques used at the attributes level, which could play a critical role in the rule generation process. In this article we attempt to solve this problem through an efficient weighted classification based on association rules algorithm, named WCBA. We also present a new pruning and prediction technique based on statistical measures to generate more accurate association rules to enhance the accuracy level of the AC classifiers. As a case study, we used WCBA to classify breast cancer instances with the help of subject matter experts from King Hussein Cancer Center (KHCC) located in Amman, Jordan. We compare WCBA with five well-known AC algorithms: CBA, CMAR, MCAR, FACA and ECBA running on two breast cancer datasets from UCI machine learning data repository. Experimental results show that WCBA, in most cases, outperformed the other AC algorithms for this case study. In addition, WCBA generates more accurate rules that contain the most efficient attributes for predicting breast cancer. WCBA algorithm aims to predict breast cancer in a patient. It serves all breast cancer patients by reducing the fear of the possibility of the recurrence of the disease and takes the necessary measures to prevent the progression of the disease and to predict breast cancer in a patient. The algorithm can be generalized to work on different domains with the help of subject matter experts. (C) 2017 Elsevier B.V. All rights reserved.