Enhancing Lung Cancer Detection Accuracy: Implementing Smote for Balanced Learning

Title

Enhancing Lung Cancer Detection Accuracy: Implementing Smote for Balanced Learning

Creator

Abraham, Jerin; Upreti, Kamal

Description

This research goal is to forecast lung cancer using machine learning, and addressing the dataset's class imbalance is a top priority. The data that was initially gathered was extremely unbalanced, with 87.38% of instances being of the minority class of lung cancer and only 12.62% being non-cancer cases. To address this imbalance, minority over-sampling through self-generated SMOTE (Synthetic Minority Over-sampling Technique) was implemented wherein there were 64.85% cases of lung cancer and 35.15% of non-lung cancer cases after deduplication. Logistic regression (LR), Gaussian naive Bayes, Support Vector Machine (SVM), Bernoulli naive Bayes, K nearest neighbors (KNN), Random Forest (RF), multi-layer perceptron, and extreme gradient boosting are among the machine learning methods that were tested. The best test performance was shown by the Random Forest and Extreme Gradient Boosting methods that achieved an accuracy of 97.3% followed by K Nearest Neighbors at 95.95%, and Multi-Layer Perceptron at 93.24%. This highlights the necessity of data balance and the ways in which these methods can improve the efficacy of predictive models for lung cancer. As such, this addition contributes to the dearly needed critical knowledge which may be a stepping stone for innovation within the domains of diagnosis and treatment medicine through machine learning. 2025 IEEE.

Source

2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.138-143

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Class Imbalance; classification; Lung cancer Prediction; Machine Learning; SMOTE

Coverage

Abraham J., Christ (Deemed to be University), Department of Computer Science, Ghaziabad, India; Upreti K., Christ (Deemed to be University), Department of Computer Science, Ghaziabad, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833152749-5;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/IC363308.2025.10956812

https://www.scopus.com/pages/publications/105003905445?origin=resultslist