Sarkar Suvobrata, Mali Kalyani
Department of Computer Science and Engineering, Dr B.C Roy Engineering College, Durgapur, West Bengal, India.
Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India.
Digit Health. 2024 Dec 10;10:20552076241297002. doi: 10.1177/20552076241297002. eCollection 2024 Jan-Dec.
Recently, numerous research studies have concentrated on employing hybrid metaheuristic approaches for the analysis and diagnosis of breast cancer which motivated us to devise a computer-driven diagnostic tool that could aid in improving the precision of clinical decision-making.
In the present study, an integrated metaheuristic machine learning approach-based predictive model was developed that can classify breast cancer into subgroups using clinicopathological data acquired from tertiary care hospitals or oncological institutes.
Monkey king evolution (MKE) was utilized to refine the hyperparameters of the support vector machine to achieve optimal settings, and genetic algorithm (GA) was used to choose the pertinent clinical and pathological attributes involved in classification before being applied to the support vector machine (SVM) classifier for prediction. A comparison was conducted between the results of the integrated MKE-GA-SVM model and those derived from conventional feature selection and hyperparameter tuning models such as GA-SVM, grid search-SVM, and SVM-recursive feature elimination (RFE). The effectiveness of the results was evaluated by applying the 10-fold cross-validation technique to the three multicentre datasets across all models. The integrated machine learning (ML) model achieved classification accuracies of 91.4%, 86.6%, and 75.5% across three clinicopathological breast cancer datasets, outperforming the existing models. The generated model performance was also assessed with notable metrics, namely F1-score, precision-recall curve, area under the ROC curve, mean square error and logarithmic loss.
Thus, the newly developed bio-inspired integrated metaheuristic model may be deployed as a surrogate diagnostic tool that allows clinicians to offer patients with better therapeutic outcomes.
最近，众多研究集中于采用混合元启发式方法进行乳腺癌的分析和诊断，这促使我们设计一种计算机驱动的诊断工具，以帮助提高临床决策的准确性。
在本研究中，开发了一种基于集成元启发式机器学习方法的预测模型，该模型可使用从三级护理医院或肿瘤研究所获取的临床病理数据将乳腺癌分类为不同亚组。
利用猴王进化算法（MKE）优化支持向量机的超参数以实现最佳设置，并使用遗传算法（GA）选择分类中涉及的相关临床和病理属性，然后将其应用于支持向量机（SVM）分类器进行预测。对集成的MKE-GA-SVM模型的结果与传统特征选择和超参数调整模型（如GA-SVM、网格搜索-SVM和SVM-递归特征消除（RFE））的结果进行了比较。通过对所有模型的三个多中心数据集应用10折交叉验证技术来评估结果的有效性。集成机器学习（ML）模型在三个临床病理乳腺癌数据集上的分类准确率分别为91.4%、86.6%和75.5%，优于现有模型。还使用显著指标（即F1分数、精确召回曲线、ROC曲线下面积、均方误差和对数损失）评估了生成模型的性能。
因此，新开发的受生物启发的集成元启发式模型可作为替代诊断工具部署，使临床医生能够为患者提供更好的治疗效果。
