Fp012 – Particle Swarm Optimization Feature Selection for Breast Cancer Recurrence Prediction

ABSTRACT:

Ladies who have recouped from bosom malignant growth (BC) dependably fear its repeat. The way that they have persevered through the meticulous treatment makes repeat their biggest dread. In any case, with current headways in innovation, early repeat expectation can enable patients to get treatment prior. The accessibility of broad information and propelled strategies make precise and quick expectation conceivable.

This examination means to look at the precision of a couple of existing information mining calculations in anticipating BC repeat. It installs a molecule swarm improvement as highlight choice into three prestigious classifiers, in particular, credulous Bayes, K-closest neighbor, and quick choice tree student, with the goal of expanding the precision level of the forecast display.

BASE PAPER: Particle Swarm Optimization Feature Selection for Breast Cancer Recurrence Prediction

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