Chaotic Duck Traveler Optimization (cDTO) Algorithm for Feature Selection in Breast Cancer Dataset Problem

Objective: 1 of every 3 individuals will be determined to have malignancy in the course their life. Currently, there are more than 3.8 million ladies who been breast United States. 2021 is practically around bend, yet there's still an ideal opportunity help confronting 2020. In this paper, chaotic based duck travel optimization (cDTO) meta-heuristic algorithm introduced classifying input images from Mammogram Image Analysis Society (MIAS) database. Methods: Linear Discriminant used extract mammogram image features. (cDTO-LDA) intrinsic remove irrelevant features and select optimal by using wavelet families Haar (harr), db4 (daubechies), bior4.4 (Biorthogonal), Symlets (SYM8), “Discrete” FIR approximation Meyer (dmey) Results: These selected evaluated quality measures such as accuracy, sensitivity, specificity, error rate that clearly shows high exactness cDTO classifier 98.5%. CSA-LDA has minimum exactness. Conclusion: Algorithm efficiency proved promising results achieved proposed for selecting best feature cancer classification.