Advanced Ensemble Machine Learning Framework for Lung Cancer Prediction: A Comprehensive Multi-Algorithmic Approach with Sophisticated Feature Engineering and Clinical Decision Support Integration

Advanced Ensemble Machine Learning Framework for Lung Cancer Prediction: A Comprehensive Multi-Algorithmic Approach with Sophisticated Feature Engineering and Clinical Decision Support Integration | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 July 2025 V1 Latest version Share on Advanced Ensemble Machine Learning Framework for Lung Cancer Prediction: A Comprehensive Multi-Algorithmic Approach with Sophisticated Feature Engineering and Clinical Decision Support Integration Authors : Sneha Sahare 0009-0002-0161-1527 , Harshala Shingne , Kaushik Roy , Ankush Sawarkar , Ankit Mahule 0000-0003-4656-1658 [email protected] , and Shweta Tumne Authors Info & Affiliations https://doi.org/10.22541/au.175332603.34577275/v1 263 views 177 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Lung cancer remains the leading cause of cancer-related mortality worldwide, with 2.21 million new cases and 1.80 million deaths annually. Existing diagnostic methods often lack accuracy, accessibility, and cost-effectiveness, especially in low-resource settings. This study proposes a robust ensemble machine learning framework for lung cancer risk prediction using a dataset of 309 patients with 15 clinical and lifestyle features. After advanced preprocessing and feature engineering, 25 variables were selected. Eleven machine learning algorithms, including Random Forest, Gradient Boosting, Neural Networks, and Support Vector Machines, were evaluated. The Hard Voting Ensemble classifier achieved the highest performance with 95.16% accuracy, 98.11% precision, 96.30% recall, 97.20% F1-score, and a Matthews Correlation Coefficient of 0.798. Stratified 5-fold cross-validation yielded a mean accuracy of 91.52% ± 5.82%. Key predictors included total symptom burden, general symptom score, and allergy status. The proposed ensemble model outperformed individual algorithms, demonstrating strong clinical relevance and potential integration into clinical decision support systems. Supplementary Material File (aa.pdf) Download 4.51 MB Information & Authors Information Version history V1 Version 1 24 July 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords clinical decision support ensemble machine learning feature engineering lung cancer prediction multi-algorithmic validation predictive oncology Authors Affiliations Sneha Sahare 0009-0002-0161-1527 Yeshwantrao Chavan College of Engineering Department of Computer Technology View all articles by this author Harshala Shingne Symbiosis International University Symbiosis Institute of Technology View all articles by this author Kaushik Roy Shri Ramdeobaba College of Engineering and Management View all articles by this author Ankush Sawarkar Shri Guru Gobind Singhji Institute of Engineering and Technology View all articles by this author Ankit Mahule 0000-0003-4656-1658 [email protected] Larsen and Toubro Limited View all articles by this author Shweta Tumne Larsen and Toubro Limited View all articles by this author Metrics & Citations Metrics Article Usage 263 views 177 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sneha Sahare, Harshala Shingne, Kaushik Roy, et al. Advanced Ensemble Machine Learning Framework for Lung Cancer Prediction: A Comprehensive Multi-Algorithmic Approach with Sophisticated Feature Engineering and Clinical Decision Support Integration. Authorea . 24 July 2025. DOI: https://doi.org/10.22541/au.175332603.34577275/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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