Modeling the Impact of Demographic, Socioeconomic, and Clinical ‎Factors on Cancer Patient Outcomes Using Multivariate Regression ‎Analysis

AL-Thabhawee, Ghassan; Abbas, Noor; Alhumaima, Ali; Abdullah, Ala'a; Obiad, Mostafa

doi:10.25673/123650

Proceedings of International Conference on Applied Innovation in IT · 2026/03/31 · Vol. 14 · Issue 1 · pp. 885–892

Modeling the Impact of Demographic, Socioeconomic, and Clinical ‎Factors on Cancer Patient Outcomes Using Multivariate Regression ‎Analysis

Ghassan AL-Thabhawee, Noor Razzaq Abbas, Ali Subhi Alhumaima, Ala'a Jalal Abdullah, Mostafa Abotaleb and Abbas Hussein Obiad

📄 Download PDF DOI: 10.25673/123650

Abstract

Knowing how demographic, socioeconomic, and scientific elements impact cancer outcomes is essential for growing robust predictive fashions. This has a look at employs multivariate regression evaluation to quantify the effect of those factors on survival and progression-unfastened survival, aiming to discover key predictors and decorate statistical modelling in oncology. A retrospective study of 673 cancer sufferers applied logistic regression for binary survival effects and Cox proportional risks models for PFS. Age, cancer degree, medical health insurance, and comorbidities were analysed. Model assessment protected goodness-of-fit exams, multicollinearity exams (VIF), and statistics standards (AIC, BIC). Advanced cancer stage (Stage III-IV) was the strongest predictor of negative outcomes, with a risk ratio (HR) of 3.21 for disease development (p < 0.001) and an odds ratio (OR) of 0.24 for survival (p < 0.001). Older age (≥60 years) and shortage of health insurance were associated with worse results, whilst marital repute and medical health insurance acted as protective factors. The models tested strong statistical significance, with most cancer stages being the most influential variable. The study highlights the importance of early detection, socioeconomic support, and accessible resources in improving outcomes. Future research should explore additional covariates and advanced techniques like machine learning to refine predictive models and inform personalized strategies.

Keywords

Multivariate Regression Cox Proportional Hazards Logistic Regression Survival Analysis Cancer Outcomes Mathematical Statistics Predictive Modelling.

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