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Improved Mixture Cure Model Using Machine Learning Approaches

Author

Listed:
  • Huina Wang

    (Department of Biostatistics, School of Public Health, Peking University, Beijing 100191, China)

  • Tian Feng

    (Department of Biostatistics, School of Public Health, Peking University, Beijing 100191, China)

  • Baosheng Liang

    (Department of Biostatistics, School of Public Health, Peking University, Beijing 100191, China)

Abstract

The mixture cure model has been widely used in medicine, public health, and bioinformatics. The traditional mixture cure model has limitations in model flexibility and handling complex structured data and big data. In recent years, some improved new methods have been developed. Through a literature review and numerical studies, this article discusses the advantages and disadvantages of the progressions of mixture cure models incorporating machine learning techniques such as SVMs for model improvements. Machine learning algorithms have advantages in model flexibility and computation. When combined with mixture cure models, they can effectively improve the performance of mixture cure models, distinguish between susceptible and non-susceptible individuals, and accurately predict the influencing factors and their magnitude of incidence and latency.

Suggested Citation

  • Huina Wang & Tian Feng & Baosheng Liang, 2025. "Improved Mixture Cure Model Using Machine Learning Approaches," Mathematics, MDPI, vol. 13(4), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:4:p:557-:d:1586433
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    References listed on IDEAS

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