article · Computers, materials & continua/Computers, materials & continua (Print)
Rapid population growth and limited diagnostic resources present critical challenges for early disease detection, notably for breast cancer, which is the second most severe cancer type. Machine learning advances can provide rapid, reliable diagnostic support to healthcare workers, helping to mitigate mortality risks. A novel feature selection algorithm combines two recent approaches, the guided whale optimiser and the dipper throated optimiser, to enhance medical data processing. Evaluated across four publicly accessible breast cancer datasets alongside several competing methods, the hybrid algorithm demonstrated notable improvements in both processing speed and diagnostic accuracy. Statistical analysis confirmed its stability and performance advantages over alternative techniques. Across the evaluations, the method achieved a peak average diagnostic prediction accuracy of 99.453 per cent while requiring an average execution time of 3.6725 seconds, outperforming all competing approaches tested.
Breast cancer is a leading cause of severe illness, and healthcare systems often struggle with resource shortages during diagnosis. Applying high-speed, highly accurate machine learning algorithms to medical datasets can help clinicians detect diseases earlier. This improves diagnostic reliability while cutting processing times, supporting efforts to reduce cancer-related fatalities.
The algorithm could enable rapid diagnostic decision-support tools for healthcare professionals interpreting breast cancer screening data. Because the findings are based on tests using four publicly available datasets, this technology remains at an early, algorithmic stage of development. Moving closer to commercial application would require integration into clinical workflows and validation within certified medical diagnostic software.
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The rapid population growth results in a crucial problem in the early detection of diseases in medical research. Among all the cancers unveiled, breast cancer is considered the second most severe cancer. Consequently, an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses. Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective, reliable, and rapid responses, which could help in decreasing the death risk. In this paper, we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers, namely, guided whale and dipper throated optimizers. The proposed algorithm is evaluated using four publicly available breast cancer datasets. The evaluation results show the effectiveness of the proposed approach from the accuracy and speed perspectives. To prove the superiority of the proposed algorithm, a set of competing feature selection algorithms were incorporated into the conducted experiments. In addition, a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed algorithm. The best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%. This result is achieved in an average time of 3.6725 s, the best result among all the competing approaches utilized in the experiments.
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DOI: 10.32604/cmc.2023.031723
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