A hybrid random forest and k-nearest neighbors approach for breast cancer detection
International Journal of Advanced Computer Research · 2024
Data mining, a crucial component of this technological advancement, involves extracting valuable insights and patterns from large datasets.Often referred to as knowledge discovery in databases (KDD), data mining encompasses various functions, including data cleaning to resolve inconsistencies, pattern recognition, visualization, and rule generation [1214].These functions can be classified based on their capabilities.Advanced computational techniques, including linear regression, logistic regression, support vector machines (SVM), Naïve Bayes (NB), decision trees (DT), k-nearest neighbors (kNN), clustering methods (such as k-Means and fuzzy c-Means), random forests (RF), and association rule mining (Apriori), have shown remarkable capabilities in analyzing complex and voluminous datasets [1315].These methods can extract meaningful patterns and make highly accurate predictions, often surpassing the diagnostic capabilities of traditional methods.The integration of data-driven approaches in medical diagnostics is motivated by the need for greaterResearch