A research on breast cancer prediction using data mining techniques

Abstract

Early detection and diagnosis of breast cancer plays a significant role in the welfare of women. The mortality rate due to breast cancer is on an all-time high. Factors such as food habits, environmental pollution, hectic lifestyle and genetics are commonly attributed to breast cancer. In order to detect and diagnose such types of cancer, intelligent systems are implemented. Automated diagnosis gets impacted by prediction accuracy when compared with surgical biopsy. Bioinformatics mining has emerged as the area of research that involves analyzing both data mining and Bioinformatics. In order to statistically find significant associations on a breast cancer data set, the result is conceivable. Using a larger data set results in discovering the correlations between a bigger set of gene. The algorithm has to be improved to perceive the interactions with low marginal. This research field affords most intelligent and reliable data mining models in breast cancer prediction and decision making. This survey reviews various data mining algorithms on large breast cancer biological datasets. The merits and demerits of various procedures and comparison of their corresponding results are presented in this work.