Lung cancer prediction with advanced graph neural networks

Title

Lung cancer prediction with advanced graph neural networks

Creator

Moozhippurath B.; Natarajan J.

Description

This research aims to enhance lung cancer prediction using advanced machine learning techniques. The major finding is that integrating graph convolutional networks (GCNs) with graph attention networks (GATs) significantly improves predictive accuracy. The problem addressed is the need for early and accurate detection of lung cancer, leveraging a dataset from Kaggle's "Lung Cancer Prediction Dataset," which includes 309 instances and 16 attributes. The proposed A-GCN with GAT model is meticulously engineered with multiple layers and hidden units, optimized through hyperparameter adjustments, early stopping mechanisms, and Adam optimization techniques. Experimental results demonstrate the model's superior performance, achieving an accuracy of 0.9454, precision of 0.9213, recall of 0.9743, and an F1 score of 0.9482. These findings highlight the model's efficacy in capturing intricate patterns within patient data, facilitating early interventions and personalized treatment plans. This research underscores the potential of graph-based methodologies in medical research, particularly for lung cancer prediction, ultimately aiming to improve patient outcomes and survival rates through proactive healthcare interventions. 2025 Institute of Advanced Engineering and Science. All rights reserved.

Source

Indonesian Journal of Electrical Engineering and Computer Science, Vol-37, No. 2, pp. 1077-1084.

Date

2025-01-01

Publisher

Institute of Advanced Engineering and Science

Subject

Graph attention network; Graph neural network; Lung cancer; Machine learning; Prediction

Coverage

Moozhippurath B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Natarajan J., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 25024752

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.11591/ijeecs.v37.i2.pp1077-1084

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210744116&doi=10.11591%2fijeecs.v37.i2.pp1077-1084&partnerID=40&md5=93cece260be04be8e7b060a60039c23b