Enhancing Lung Cancer Survival Prediction: A Comparative Study of Hybrid Cox-SVM and Logistic-SVM Models | ||
| التجارة والتمويل | ||
| Volume 45, Issue 2, June 2025, Pages 342-377 PDF (1.55 M) | ||
| DOI: 10.21608/caf.2025.435002 | ||
| Author | ||
| Abdelreheem Awad Bassuny | ||
| Lecturer at the Higher Institute of A Management in Mahalla El-Kubra | ||
| Abstract | ||
| The research compares the survival performance of hybrid survival models that merge traditional statistical techniques with machine learning to forecast survival outcomes in lung cancer patients. The study compares a group of 165 patients from Tanta Oncology Institute and Kafr El-Sheikh Chest Hospital between 2020 and 2024 by contrasting individual models—Cox Regression, Logistic Regression, and Support Vector Machines (SVM)—with two hybrid models: Cox-SVM and Logistic-SVM. High-risk predictors such as smoking, occupation, age, treatment modalities, COVID-19 infection, and disease stage were found and modeled through Kaplan-Meier analysis for feature selection. The results show that the Cox-SVM hybrid model gives the best results among all the other models with a classification accuracy of 94.5%, sensitivity of 90%, specificity of 96%, and misclassification rate of 5.45%. The Logistic-SVM hybrid then comes into play with a 90.91% accuracy, and then the individual models (Cox: 80.6%, Logistic: 67.88%, SVM: 81.8%). Hybrid model performance is because Cox's hazard-based model combines SVM's capability to work with non-linear relationships and produce better predictive accuracy and clinical relevance. Despite small sample size limitations and omission of certain variables, these results suggest that hybrid approaches in survival analysis are a valuable resource in personalized medicine. These models must be validated with larger, more diverse datasets and other prognostic factors in future research | ||
| Keywords | ||
| Survival Analysis; Hybrid Models; Kaplan-Meier; Machine Learning; Cox Regression | ||
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