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Enhancing Cure Rate Analysis Through Integration of Machine Learning Models: A Comparative Study.

Wisdom Aselisewine1, Suvra Pal1,2

  • 1Department of Mathematics, University of Texas at Arlington, Texas, USA 76019.

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Summary
This summary is machine-generated.

Machine learning (ML) models enhance cure rate predictions by integrating with cure models. Comparing five ML algorithms with traditional methods shows ML improves cure rate estimation accuracy.

Keywords:
EM algorithmMachine learningPredictive accuracyProportional hazardmixture cure model

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Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Cure rate models are vital in medicine, finance, and reliability.
  • Integrating machine learning (ML) with cure models shows promise for improved prediction.
  • Existing research often explores single ML algorithms, lacking comparative studies.

Purpose of the Study:

  • To comprehensively compare the performance of various ML algorithms within mixture cure models.
  • To evaluate the contribution of ML to cure rate estimation accuracy.
  • To address the gap in comparative studies of ML algorithms for cure rate modeling.

Main Methods:

  • Incorporated five ML algorithms (gradient boosting, neural networks, SVM, random forests, decision trees) into mixture cure models.
  • Utilized logistic and spline-based regression cure models for comparison.
  • Employed an expectation maximization algorithm for parameter estimation.
  • Conducted extensive simulations and analyzed real cutaneous melanoma data.

Main Results:

  • ML models significantly contributed to improving cure rate estimation accuracy.
  • The study provided a robust comparison of different ML algorithms in cure modeling.
  • Both simulation and real-world data analyses supported the benefits of ML integration.

Conclusions:

  • Machine learning models offer a valuable enhancement to traditional cure rate models.
  • Comparative analysis demonstrates the effectiveness of ML in improving predictive accuracy for cure rates.
  • The findings support the broader adoption of ML techniques in cure rate modeling across disciplines.