Optimizing cancer classification: a hybrid RDO-XGBoost approach for feature selection and predictive insights.

IF 4.6 2区 医学 Q2 IMMUNOLOGY
Abrar Yaqoob, Navneet Kumar Verma, Rabia Musheer Aziz, Mohd Asif Shah
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引用次数: 0

Abstract

The identification of relevant biomarkers from high-dimensional cancer data remains a significant challenge due to the complexity and heterogeneity inherent in various cancer types. Conventional feature selection methods often struggle to effectively navigate the vast solution space while maintaining high predictive accuracy. In response to these challenges, we introduce a novel feature selection approach that integrates Random Drift Optimization (RDO) with XGBoost, specifically designed to enhance the performance of cancer classification tasks. Our proposed framework not only improves classification accuracy but also offers valuable insights into the underlying biological mechanisms driving cancer progression. Through comprehensive experiments conducted on real-world cancer datasets, including Central Nervous System (CNS), Leukemia, Breast, and Ovarian cancers, we demonstrate the efficacy of our method in identifying a smaller subset of unique and relevant genes. This selection results in significantly improved classification efficiency and accuracy. When compared with popular classifiers such as Support Vector Machine, K-Nearest Neighbor, and Naive Bayes, our approach consistently outperforms these models in terms of both accuracy and F-measure metrics. For instance, our framework achieved an accuracy of 97.24% in the CNS dataset, 99.14% in Leukemia, 95.21% in Ovarian, and 87.62% in Breast cancer, showcasing its robustness and effectiveness across different types of cancer data. These results underline the potential of our RDO-XGBoost framework as a promising solution for feature selection in cancer data analysis, offering enhanced predictive performance and valuable biological insights.

优化癌症分类:用于特征选择和预测洞察的 RDO-XGBoost 混合方法。
由于各种癌症类型固有的复杂性和异质性,从高维癌症数据中识别相关生物标记物仍然是一项重大挑战。传统的特征选择方法往往难以在保持高预测准确性的同时有效地驾驭广阔的解空间。为了应对这些挑战,我们引入了一种新颖的特征选择方法,它将随机漂移优化(RDO)与 XGBoost 整合在一起,专门用于提高癌症分类任务的性能。我们提出的框架不仅能提高分类准确性,还能为了解驱动癌症进展的潜在生物机制提供宝贵的见解。通过在真实世界癌症数据集(包括中枢神经系统(CNS)、白血病、乳腺癌和卵巢癌)上进行的全面实验,我们证明了我们的方法在识别较小的独特相关基因子集方面的功效。这种选择大大提高了分类效率和准确性。与支持向量机、K-近邻和 Naive Bayes 等流行分类器相比,我们的方法在准确率和 F-measure 指标方面始终优于这些模型。例如,我们的框架在中枢神经系统数据集中的准确率达到了 97.24%,在白血病中达到了 99.14%,在卵巢癌中达到了 95.21%,在乳腺癌中达到了 87.62%,显示了它在不同类型癌症数据中的鲁棒性和有效性。这些结果凸显了我们的 RDO-XGBoost 框架作为癌症数据分析中特征选择的一种有前途的解决方案的潜力,它能提供更高的预测性能和有价值的生物学见解。
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来源期刊
CiteScore
10.50
自引率
1.70%
发文量
207
审稿时长
1 months
期刊介绍: Cancer Immunology, Immunotherapy has the basic aim of keeping readers informed of the latest research results in the fields of oncology and immunology. As knowledge expands, the scope of the journal has broadened to include more of the progress being made in the areas of biology concerned with biological response modifiers. This helps keep readers up to date on the latest advances in our understanding of tumor-host interactions. The journal publishes short editorials including "position papers," general reviews, original articles, and short communications, providing a forum for the most current experimental and clinical advances in tumor immunology.
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