ItemConference Paper

Enhancing Diagnostic Precision in Lung Cancer Detection Using Smote-Based Balancing Techniques

Title
Enhancing Diagnostic Precision in Lung Cancer Detection Using Smote-Based Balancing Techniques
Creator
Upreti, Kamal; Radhakrishnan, G.V.; Shankar, Uma; Tiwari, Akhilesh; Gupta, Komal; Rajendran, Vijayalaxmi
Description
Worldwide, Lung cancer is the primary cause of death from cancer, and chances of surviving are considerably raised by early detection. While traditional diagnostic approaches heavily rely on imaging and specialized infrastructure, they often fail to serve low-resource or early screening environments. In this work, based on deep learning, lightweight framework for detecting lung cancer from structured survey data is presented. The research tackles the prevalent the problem of class disparity using the Synthetic Minority Over-sampling Technique (SMOTE), enhancing the sensitivity of predictive models. A comparative evaluation was conducted across six models Logistic Regression, SVM, KNN, Naive Bayes, Random Forest, and XGBoost. Among these, Random Forest and XGBoost achieved 95% accuracy, 0.98 recall, and ROC-AUC scores of 0.9943 and 0.9835 respectively. The proposed hybrid ensemble model (Random Forest + XGBoost) outperformed all with 96% accuracy, 0.95 precision, 0.98 recall, and a ROC-AUC score of 0.9961. These findings demonstrate that the hybrid strategy is effective in providing high diagnostic precision using clinical survey data that is not imaging. 2025 IEEE.
Source
2025 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Hybrid Ensemble; Lung Cancer Detection; Machine Learning; Medical AI; Non-Imaging Prediction
Coverage
Upreti K., Christ University, Department of Computer Science, Ghaziabad, India; Radhakrishnan G.V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Shankar U., Faculty of Management and Social Sciences, Qaiwan International University, Kurdistan, Sulaymaniyah, Iraq; Tiwari A., Christ University, Department of Business and Management, Ghaziabad, India; Gupta K., Accenture, Banglore, India; Rajendran V., School of Business and Management, Christ University, Ghaziabad, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152989-5;
Format
online
Language
English
Type
Conference paper

Citation

Upreti, Kamal; Radhakrishnan, G.V.; Shankar, Uma; Tiwari, Akhilesh; Gupta, Komal; Rajendran, Vijayalaxmi, “Enhancing Diagnostic Precision in Lung Cancer Detection Using Smote-Based Balancing Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25763.

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