Improved Mixture Cure Model Using Machine Learning Approaches
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/4/557 |
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| author | Huina Wang Tian Feng Baosheng Liang |
| author_facet | Huina Wang Tian Feng Baosheng Liang |
| author_sort | Huina Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0ef83777bd8b47a899b8136aadd447b2 |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-0ef83777bd8b47a899b8136aadd447b22025-08-20T03:12:05ZengMDPI AGMathematics2227-73902025-02-0113455710.3390/math13040557Improved Mixture Cure Model Using Machine Learning ApproachesHuina Wang0Tian Feng1Baosheng Liang2Department of Biostatistics, School of Public Health, Peking University, Beijing 100191, ChinaDepartment of Biostatistics, School of Public Health, Peking University, Beijing 100191, ChinaDepartment of Biostatistics, School of Public Health, Peking University, Beijing 100191, ChinaThe 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.https://www.mdpi.com/2227-7390/13/4/557mixture cure modelsurvival analysismachine learningmodel improvement |
| spellingShingle | Huina Wang Tian Feng Baosheng Liang Improved Mixture Cure Model Using Machine Learning Approaches Mathematics mixture cure model survival analysis machine learning model improvement |
| title | Improved Mixture Cure Model Using Machine Learning Approaches |
| title_full | Improved Mixture Cure Model Using Machine Learning Approaches |
| title_fullStr | Improved Mixture Cure Model Using Machine Learning Approaches |
| title_full_unstemmed | Improved Mixture Cure Model Using Machine Learning Approaches |
| title_short | Improved Mixture Cure Model Using Machine Learning Approaches |
| title_sort | improved mixture cure model using machine learning approaches |
| topic | mixture cure model survival analysis machine learning model improvement |
| url | https://www.mdpi.com/2227-7390/13/4/557 |
| work_keys_str_mv | AT huinawang improvedmixturecuremodelusingmachinelearningapproaches AT tianfeng improvedmixturecuremodelusingmachinelearningapproaches AT baoshengliang improvedmixturecuremodelusingmachinelearningapproaches |