Utilizing patient data: A tutorial on predicting second cancer with machine learning models
Abstract Background The article explores the potential risk of secondary cancer (SC) due to radiation therapy (RT) and highlights the necessity for new modeling techniques to mitigate this risk. Methods By employing machine learning (ML) models, specifically decision trees, in the research process,...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-09-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70231 |
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| _version_ | 1849716358919487488 |
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| author | Hossein Sadeghi Fatemeh Seif Erfan Hatamabadi Farahani Soraya Khanmohammadi Shahla Nahidinezhad |
| author_facet | Hossein Sadeghi Fatemeh Seif Erfan Hatamabadi Farahani Soraya Khanmohammadi Shahla Nahidinezhad |
| author_sort | Hossein Sadeghi |
| collection | DOAJ |
| description | Abstract Background The article explores the potential risk of secondary cancer (SC) due to radiation therapy (RT) and highlights the necessity for new modeling techniques to mitigate this risk. Methods By employing machine learning (ML) models, specifically decision trees, in the research process, a practical framework is established for forecasting the occurrence of SC using patient data. Results & Discussion This framework aids in categorizing patients into high‐risk or low‐risk groups, thereby enabling personalized treatment plans and interventions. The paper also underscores the many factors that contribute to the likelihood of SC, such as radiation dosage, patient age, and genetic predisposition, while emphasizing the limitations of current models in encompassing all relevant parameters. These limitations arise from the non‐linear dependencies between variables and the failure to consider factors such as genetics, hormones, lifestyle, radiation from secondary particles, and imaging dosage. To instruct and assess ML models for predicting the occurrence of SC based on patient data, the paper utilizes a dataset consisting of instances and attributes. Conclusion The practical implications of this research lie in enhancing our understanding and prediction of SC following RT, facilitating personalized treatment approaches, and establishing a framework for leveraging patient data within the realm of ML models. |
| format | Article |
| id | doaj-art-e6a193eba3bb4ee29bf14f1a0f2fae78 |
| institution | DOAJ |
| issn | 2045-7634 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-e6a193eba3bb4ee29bf14f1a0f2fae782025-08-20T03:13:01ZengWileyCancer Medicine2045-76342024-09-011318n/an/a10.1002/cam4.70231Utilizing patient data: A tutorial on predicting second cancer with machine learning modelsHossein Sadeghi0Fatemeh Seif1Erfan Hatamabadi Farahani2Soraya Khanmohammadi3Shahla Nahidinezhad4Department of Physics, Faculty of Sciences Arak University Arak IranDepartment of Radiotherapy and Medical Physics Arak University of Medical Sciences & Khansari Hospital Arak IranDepartment of Physics, Faculty of Sciences Arak University Arak IranIndustrial and Systems Engineering, Tarbiat Modares University Tehran IranDepartment of Physics, Faculty of Sciences Arak University Arak IranAbstract Background The article explores the potential risk of secondary cancer (SC) due to radiation therapy (RT) and highlights the necessity for new modeling techniques to mitigate this risk. Methods By employing machine learning (ML) models, specifically decision trees, in the research process, a practical framework is established for forecasting the occurrence of SC using patient data. Results & Discussion This framework aids in categorizing patients into high‐risk or low‐risk groups, thereby enabling personalized treatment plans and interventions. The paper also underscores the many factors that contribute to the likelihood of SC, such as radiation dosage, patient age, and genetic predisposition, while emphasizing the limitations of current models in encompassing all relevant parameters. These limitations arise from the non‐linear dependencies between variables and the failure to consider factors such as genetics, hormones, lifestyle, radiation from secondary particles, and imaging dosage. To instruct and assess ML models for predicting the occurrence of SC based on patient data, the paper utilizes a dataset consisting of instances and attributes. Conclusion The practical implications of this research lie in enhancing our understanding and prediction of SC following RT, facilitating personalized treatment approaches, and establishing a framework for leveraging patient data within the realm of ML models.https://doi.org/10.1002/cam4.70231decision treesmachine learningprecision medicineradiation dosage |
| spellingShingle | Hossein Sadeghi Fatemeh Seif Erfan Hatamabadi Farahani Soraya Khanmohammadi Shahla Nahidinezhad Utilizing patient data: A tutorial on predicting second cancer with machine learning models Cancer Medicine decision trees machine learning precision medicine radiation dosage |
| title | Utilizing patient data: A tutorial on predicting second cancer with machine learning models |
| title_full | Utilizing patient data: A tutorial on predicting second cancer with machine learning models |
| title_fullStr | Utilizing patient data: A tutorial on predicting second cancer with machine learning models |
| title_full_unstemmed | Utilizing patient data: A tutorial on predicting second cancer with machine learning models |
| title_short | Utilizing patient data: A tutorial on predicting second cancer with machine learning models |
| title_sort | utilizing patient data a tutorial on predicting second cancer with machine learning models |
| topic | decision trees machine learning precision medicine radiation dosage |
| url | https://doi.org/10.1002/cam4.70231 |
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