Adaptive Fuzzy C-Means with logit boost distributed clustering for cancer detection with protein sequences

The Adaptive Fuzzy C-Means with Logit Boost Distributed Clustering (AFC-LBDC) technique is introduced to enhance cancer detection promptly. The various conventional techniques often struggle to improve cancer detection due to their high complexity effectively. In contrast, the AFC-LBDC technique groups similar protein sequences to get better accuracy in cancer detection. Initially, a large protein dataset is divided into ‘C’ number of local clusters using an adaptive Fuzzy C-Means distributed clustering approach. For any protein sequences that are not assigned to a group, the Bayesian probability is computed to find the higher chance of the protein sequence becoming a member of a specific cluster. The Logit Boost technique is applied to improve the clustering performance further, which combines the number of local clusters to make a global cluster. The proposed AFC-LBDC method demonstrates high accuracy rates of 96%, 88%, and 86% for the P53, BRCA2, and HRAS cancer datasets, respectively. Comparative evaluation reveals that AFC-LBDC reduces cancer detection time by up to 31% compared to existing methods, achieving a 20% and 31% reduction over the RaNC and IDMPhyChm-Ens methods for the P53 dataset, 19% and 31% for BRCA2, and 22% and 32% for HRAS. Likewise, the proposed method significantly lowers the false positive rate, with reductions of 27% and 39% for P53, 28% and 36% for BRCA2, and 23% and 31% for HRAS, compared to RaNC and IDMPhyChm-Ens, respectively. In addition, AFC-LBDC minimises space complexity by up to 44%, with 27% and 39% reductions for P53, 24% and 42% for BRCA2, and 22% and 44% for HRAS datasets. These results collectively indicate the superior performance and efficiency of AFC-LBDC in cancer gene detection. The global clustering result improves the cancer detection accuracy and minimises the false positive rate.