Lung cancer prognosis based on salivary biomarkers using Graph Convolutional Networks

Gilang Raka Rayuda Dewa; Raisa Imani Sani; Ady Syamsuri; Charles Agustin; Muhammad Agni Catur Bhakti; Ariana Tulus  Purnomo

doi:10.24246/aiti.v23i2.245-260

Authors

Gilang Raka Rayuda Dewa Sampoerna University https://orcid.org/0000-0002-9922-8501
Raisa Imani Sani Sampoerna University
Ady Syamsuri Sampoerna University
Charles Agustin Sampoerna University https://orcid.org/0009-0001-2075-0920
Muhammad Agni Catur Bhakti Sampoerna University
Ariana Tulus Purnomo Sampoerna University

DOI:

https://doi.org/10.24246/aiti.v23i2.245-260

Keywords:

classification, lung cancer, Graph Convolution Networks, salivary biomarkers, feature selection

Abstract

Lung cancer remains the leading factors that incur cancer-related deaths worldwide, mainly due to late-stage detection. In 2022, lung cancer affected almost 2.5 million people, with mortality of more than 1.8 million. However, existing prognostic methods are typically invasive, costly, and time-consuming, hindering effective early detection. Therefore, this research proposes a non-invasive prognostic approach using salivary biomarkers to detect lung cancer via Graph Convolutional Networks (GCNs). By transforming features into graph node representations, the proposed algorithm can model feature dependencies and topological relationships, enabling more effective pattern recognition than conventional classifiers. The proposed algorithm also applies feature selection to reduce computational complexity. The evaluation results show that the proposed algorithm achieves 95.65% accuracy, a macro F1-score of 95.62%, and a Matthews Correlation Coefficient of 0.9434. A comparative analysis shows that the proposed algorithm outperforms other graph-based architectures in terms of classification performance and computational complexity.

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