A Fusion-Based Machine Learning Framework for Lung Cancer Survival Prediction Using Clinical and Lifestyle Data

Abstract

Lung cancer is one of the deadliest diseases worldwide, highlighting the criticality of precise survival prediction models. This work proposes an exhaustive fusion-based machine learning approach for lung cancer survival prediction using heterogeneous features such as clinical indicators, demographic information, and lifestyle factors. A publicly available dataset of more than 800,000 records was pre-processed, statistically analysed, and dimensionally reduced for computational tractability. Feature-level fusion was used to merge multivariate features, after which decision-level fusion was implemented through soft voting ensembles. Five fusion configurations using Logistic Regression, Random Forest, Support Vector Machine, k-Nearest Neighbours, and Naive Bayes classifiers were evaluated. It was noted that the simpler combinations like Logistic Regression and Random Forest worked better than larger ensembles, with accuracy of 70% and AUC of 0.61 after class balancing. Correlation and statistical analysis also showed weak linear relationships with survival, underscoring the need for non-linear modelling strategies. Every fusion model was assessed with ROC curves and confusion matrices, providing an overall view of prediction strength. The study demonstrates that fusion techniques can significantly improve survival prediction in lung cancer patients and can be the foundation for actual clinical decision support systems.