Scholarlink

Background <p>Survival analysis is widely used to predict time-to-event outcomes, with the Cox regression model being a standard approach. However, machine learning methods such as Random Survival Forests (RSF) can capture complex, non-linear relationships that traditional models may miss.</p> Objective <p>This study compared the predictive performance of RSF and Cox regression in modelling tuberculosis (TB) mortality.</p> Methods <p>We conducted a retrospective study of TB patients treated at the East London Central Clinic in South Africa. Patient data included demographic, clinical, and treatment-related variables. Model performance was evaluated using five metrics (C-index, Brier Score, Integrated Brier Score, Integrated Absolute Error, and Integrated Squared Error) along with time-dependent receiver operating characteristic (ROC) curves. Variable importance was assessed to identify key predictors.</p> Results <p>The RSF model consistently outperformed the Cox model across all evaluation metrics. RSF achieved a higher integrated AUC (0.815 vs. 0.652) and lower prediction error (IBS = 0.235 vs. 0.261). Important predictors of mortality included age, sex, weight, and disease class, with RSF capturing their time-dependent effects more accurately. The cumulative case/dynamic control ROC curve showed the strongest predictive accuracy at 120 days (AUC = 0.856).</p> Conclusion <p>RSF demonstrated superior predictive accuracy compared with Cox regression in modelling TB mortality. Its ability to account for non-linear and time-dependent effects makes it a potentially useful tool for improving risk prediction and guiding patient management in TB care.</p> Clinical trial <p>Not applicable.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning prediction of tuberculosis mortality: a comparative analysis of random survival forest and cox regression models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月17日

查看全文
  • Azeez Adeboye,
  • Osuji Georgeleen,
  • Adelabu Olusesan,
  • Noel Colin

摘要

Background

Survival analysis is widely used to predict time-to-event outcomes, with the Cox regression model being a standard approach. However, machine learning methods such as Random Survival Forests (RSF) can capture complex, non-linear relationships that traditional models may miss.

Objective

This study compared the predictive performance of RSF and Cox regression in modelling tuberculosis (TB) mortality.

Methods

We conducted a retrospective study of TB patients treated at the East London Central Clinic in South Africa. Patient data included demographic, clinical, and treatment-related variables. Model performance was evaluated using five metrics (C-index, Brier Score, Integrated Brier Score, Integrated Absolute Error, and Integrated Squared Error) along with time-dependent receiver operating characteristic (ROC) curves. Variable importance was assessed to identify key predictors.

Results

The RSF model consistently outperformed the Cox model across all evaluation metrics. RSF achieved a higher integrated AUC (0.815 vs. 0.652) and lower prediction error (IBS = 0.235 vs. 0.261). Important predictors of mortality included age, sex, weight, and disease class, with RSF capturing their time-dependent effects more accurately. The cumulative case/dynamic control ROC curve showed the strongest predictive accuracy at 120 days (AUC = 0.856).

Conclusion

RSF demonstrated superior predictive accuracy compared with Cox regression in modelling TB mortality. Its ability to account for non-linear and time-dependent effects makes it a potentially useful tool for improving risk prediction and guiding patient management in TB care.

Clinical trial

Not applicable.