Adaptive Fuzzy C-Means with logit boost distributed clustering for cancer detection with protein sequences
Abstract 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 tech...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-07485-1 |
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| author | K. Thenmozhi M. Pyingkodi V. S. Prakash Kripa Josten S. Manju Priya J. Vennila |
| author_facet | K. Thenmozhi M. Pyingkodi V. S. Prakash Kripa Josten S. Manju Priya J. Vennila |
| author_sort | K. Thenmozhi |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-e899bec86dee453eafeee5d84bacfd77 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-e899bec86dee453eafeee5d84bacfd772025-08-20T03:46:19ZengSpringerDiscover Applied Sciences3004-92612025-07-017812510.1007/s42452-025-07485-1Adaptive Fuzzy C-Means with logit boost distributed clustering for cancer detection with protein sequencesK. Thenmozhi0M. Pyingkodi1V. S. Prakash2Kripa Josten3S. Manju Priya4J. Vennila5School of Computer Science and Engineering, RV UniversityDepartment of MCA, Kongu Engineering CollegeDepartment of Computer Science, Kristu Jayanti College (Autonomous)Manipal College of Health Professions, Manipal Academy of Higher EducationSchool of Computer Science and Applications, Reva UniversityStatistics, Manipal College of Health Professions, Manipal Academy of Higher EducationAbstract 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.https://doi.org/10.1007/s42452-025-07485-1Protein sequencesCancer detectionAdaptive Fuzzy C-Means clusteringJaccard similarityLogit boost techniqueGlobal cluster |
| spellingShingle | K. Thenmozhi M. Pyingkodi V. S. Prakash Kripa Josten S. Manju Priya J. Vennila Adaptive Fuzzy C-Means with logit boost distributed clustering for cancer detection with protein sequences Discover Applied Sciences Protein sequences Cancer detection Adaptive Fuzzy C-Means clustering Jaccard similarity Logit boost technique Global cluster |
| title | Adaptive Fuzzy C-Means with logit boost distributed clustering for cancer detection with protein sequences |
| title_full | Adaptive Fuzzy C-Means with logit boost distributed clustering for cancer detection with protein sequences |
| title_fullStr | Adaptive Fuzzy C-Means with logit boost distributed clustering for cancer detection with protein sequences |
| title_full_unstemmed | Adaptive Fuzzy C-Means with logit boost distributed clustering for cancer detection with protein sequences |
| title_short | Adaptive Fuzzy C-Means with logit boost distributed clustering for cancer detection with protein sequences |
| title_sort | adaptive fuzzy c means with logit boost distributed clustering for cancer detection with protein sequences |
| topic | Protein sequences Cancer detection Adaptive Fuzzy C-Means clustering Jaccard similarity Logit boost technique Global cluster |
| url | https://doi.org/10.1007/s42452-025-07485-1 |
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