A Novel Hybrid Dynamic Harris Hawks Optimized Gated Recurrent Unit Approach for Breast Cancer Prediction
Abstract The breast cancer (BC) prediction is improved through the machine learning (ML) techniques. In this study, we develop an innovative forecasting framework called the Dynamic Harris Hawks Optimized Gated Recurrent Unit (DHH-GRU) for the prediction of BC. It combines the Gated Recurrent Unit (...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-01-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-024-00712-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832594441535225856 |
|---|---|
| author | Rajesh Natarajan Sujatha Krishna H. L. Gururaj Francesco Flammini Badria Sulaiman Alfurhood C. M. Naveen Kumar |
| author_facet | Rajesh Natarajan Sujatha Krishna H. L. Gururaj Francesco Flammini Badria Sulaiman Alfurhood C. M. Naveen Kumar |
| author_sort | Rajesh Natarajan |
| collection | DOAJ |
| description | Abstract The breast cancer (BC) prediction is improved through the machine learning (ML) techniques. In this study, we develop an innovative forecasting framework called the Dynamic Harris Hawks Optimized Gated Recurrent Unit (DHH-GRU) for the prediction of BC. It combines the Gated Recurrent Unit (GRU) and Harris Hawks Optimization (HHO) methods. We gathered data and a training set that included the Wisconsin diagnostic BC (WDBC) dataset, which contains 569 patients with malignant and beginning cases. The collected data were pre-processed using min–max normalization, and important features were extracted by Fast Fourier transform (FFT) and the process of reducing the dimensionality with principal component analysis (PCA). Decimal scaling is employed to equalize the various feature effects. The proposed DHH-GRU technique incorporated the GRU for capturing sequential connections on temporal medical information, and the optimization process, DHH optimization, is utilized. The proposed method's effectiveness is compared and estimated with various existing techniques in terms of log-loss (0.06%), accuracy (98.05%), precision (98.09%), F1-score (98.28%), and recall (98.15%). The proposed DHH-GRU method has a more predictive ability with the sequential dependency in capturing GRU and DHH optimization’s combined behaviour of hunting. This method significantly improved the accuracy of BC prediction. |
| format | Article |
| id | doaj-art-ce1bbbaacfba4183a6541ff36a472c7e |
| institution | Kabale University |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-ce1bbbaacfba4183a6541ff36a472c7e2025-01-19T12:38:11ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-01-0118111610.1007/s44196-024-00712-4A Novel Hybrid Dynamic Harris Hawks Optimized Gated Recurrent Unit Approach for Breast Cancer PredictionRajesh Natarajan0Sujatha Krishna1H. L. Gururaj2Francesco Flammini3Badria Sulaiman Alfurhood4C. M. Naveen Kumar5Information Technology Department, College of Computing and Information Sciences, University of Technology and Applied SciencesInformation Technology Department, College of Computing and Information Sciences, University of Technology and Applied SciencesDepartment of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher EducationIDSIA USI-SUPSI, University of Applied Sciences and Arts of Southern SwitzerlandDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Computer Science and Business Systems, Malanad College of EngineeringAbstract The breast cancer (BC) prediction is improved through the machine learning (ML) techniques. In this study, we develop an innovative forecasting framework called the Dynamic Harris Hawks Optimized Gated Recurrent Unit (DHH-GRU) for the prediction of BC. It combines the Gated Recurrent Unit (GRU) and Harris Hawks Optimization (HHO) methods. We gathered data and a training set that included the Wisconsin diagnostic BC (WDBC) dataset, which contains 569 patients with malignant and beginning cases. The collected data were pre-processed using min–max normalization, and important features were extracted by Fast Fourier transform (FFT) and the process of reducing the dimensionality with principal component analysis (PCA). Decimal scaling is employed to equalize the various feature effects. The proposed DHH-GRU technique incorporated the GRU for capturing sequential connections on temporal medical information, and the optimization process, DHH optimization, is utilized. The proposed method's effectiveness is compared and estimated with various existing techniques in terms of log-loss (0.06%), accuracy (98.05%), precision (98.09%), F1-score (98.28%), and recall (98.15%). The proposed DHH-GRU method has a more predictive ability with the sequential dependency in capturing GRU and DHH optimization’s combined behaviour of hunting. This method significantly improved the accuracy of BC prediction.https://doi.org/10.1007/s44196-024-00712-4Machine learning (ML)Dynamic Harris Hawks Optimized Gated Recurrent Unit (DHH-GRU)Breast cancer (BC) |
| spellingShingle | Rajesh Natarajan Sujatha Krishna H. L. Gururaj Francesco Flammini Badria Sulaiman Alfurhood C. M. Naveen Kumar A Novel Hybrid Dynamic Harris Hawks Optimized Gated Recurrent Unit Approach for Breast Cancer Prediction International Journal of Computational Intelligence Systems Machine learning (ML) Dynamic Harris Hawks Optimized Gated Recurrent Unit (DHH-GRU) Breast cancer (BC) |
| title | A Novel Hybrid Dynamic Harris Hawks Optimized Gated Recurrent Unit Approach for Breast Cancer Prediction |
| title_full | A Novel Hybrid Dynamic Harris Hawks Optimized Gated Recurrent Unit Approach for Breast Cancer Prediction |
| title_fullStr | A Novel Hybrid Dynamic Harris Hawks Optimized Gated Recurrent Unit Approach for Breast Cancer Prediction |
| title_full_unstemmed | A Novel Hybrid Dynamic Harris Hawks Optimized Gated Recurrent Unit Approach for Breast Cancer Prediction |
| title_short | A Novel Hybrid Dynamic Harris Hawks Optimized Gated Recurrent Unit Approach for Breast Cancer Prediction |
| title_sort | novel hybrid dynamic harris hawks optimized gated recurrent unit approach for breast cancer prediction |
| topic | Machine learning (ML) Dynamic Harris Hawks Optimized Gated Recurrent Unit (DHH-GRU) Breast cancer (BC) |
| url | https://doi.org/10.1007/s44196-024-00712-4 |
| work_keys_str_mv | AT rajeshnatarajan anovelhybriddynamicharrishawksoptimizedgatedrecurrentunitapproachforbreastcancerprediction AT sujathakrishna anovelhybriddynamicharrishawksoptimizedgatedrecurrentunitapproachforbreastcancerprediction AT hlgururaj anovelhybriddynamicharrishawksoptimizedgatedrecurrentunitapproachforbreastcancerprediction AT francescoflammini anovelhybriddynamicharrishawksoptimizedgatedrecurrentunitapproachforbreastcancerprediction AT badriasulaimanalfurhood anovelhybriddynamicharrishawksoptimizedgatedrecurrentunitapproachforbreastcancerprediction AT cmnaveenkumar anovelhybriddynamicharrishawksoptimizedgatedrecurrentunitapproachforbreastcancerprediction AT rajeshnatarajan novelhybriddynamicharrishawksoptimizedgatedrecurrentunitapproachforbreastcancerprediction AT sujathakrishna novelhybriddynamicharrishawksoptimizedgatedrecurrentunitapproachforbreastcancerprediction AT hlgururaj novelhybriddynamicharrishawksoptimizedgatedrecurrentunitapproachforbreastcancerprediction AT francescoflammini novelhybriddynamicharrishawksoptimizedgatedrecurrentunitapproachforbreastcancerprediction AT badriasulaimanalfurhood novelhybriddynamicharrishawksoptimizedgatedrecurrentunitapproachforbreastcancerprediction AT cmnaveenkumar novelhybriddynamicharrishawksoptimizedgatedrecurrentunitapproachforbreastcancerprediction |