Genetic instance-based learner algorithm for lymphography cancer diagnosis

Abstract

Lymphography involvement is a fundamental indicator of cancer progression, making the precise classification of lymphographic findings essential for reliable diagnosis and treatment decision-making. This study presents an embedded approach for lymphography cancer detection that synergistically combines a genetic algorithm (GA) and an instance-based learning classifier. Unlike prior GA hybrids, this approach incorporates GA feature selection with K*, a relative-entropy-based approach that handles noisy, small-sample-size, class-imbalance, and redundant features in lymphography data, rather than the traditional hybrid approach that used KNN with a geometric distance metric (typically Euclidean or Manhattan). Although vital for diagnosing lymphatic diseases, lymphography poses challenges because of the data it generates. To address these challenges, the proposed approach integrates the strengths of GA to optimize the most important features. Instance-based learning (i.e., K* classifier) is used for effective pattern recognition and to classify lymph nodes as benign, normal, metastatic, malignant, or fibrotic. Extensive experimental results on a real-world dataset demonstrated that the combination significantly outperformed state-of-the-art classification algorithms and other hybrid approaches. The efficiency of the proposed approach is evaluated using the most widely used computational metrics, yielding promising results for diagnosing lymphatic diseases. This work highlights the potential of an evolutionary-based embedded classifier for classifying complex biomedical data.

Keywords

Cancer diagnosis / Data mining / Lymphography / Metaheuristic / Parameter tuning

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