Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques
Objectives The objective of this research is to apply machine learning (ML) algorithms to predict the survival of cervical cancer patients. The aim was to address the limitations of traditional statistical methods, which often fail to provide accurate answers due to the complexity of the problem. Me...
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| Format: | Article |
| Language: | English |
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The Korean Society of Medical Informatics
2024-01-01
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| Series: | Healthcare Informatics Research |
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| Online Access: | http://e-hir.org/upload/pdf/hir-2024-30-1-60.pdf |
| _version_ | 1797306824909651968 |
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| author | Intorn Chanudom Ekkasit Tharavichitkul Wimalin Laosiritaworn |
| author_facet | Intorn Chanudom Ekkasit Tharavichitkul Wimalin Laosiritaworn |
| author_sort | Intorn Chanudom |
| collection | DOAJ |
| description | Objectives The objective of this research is to apply machine learning (ML) algorithms to predict the survival of cervical cancer patients. The aim was to address the limitations of traditional statistical methods, which often fail to provide accurate answers due to the complexity of the problem. Methods This research employed visualization techniques for initial data understanding. Subsequently, ML algorithms were used to develop both classification and regression models for survival prediction. In the classification models, we trained the algorithms to predict the time interval between the initial diagnosis and the patient’s death. The intervals were categorized as “<6 months,” “6 months to 3 years,” “3 years to 5 years,” and “>5 years.” The regression model aimed to predict survival time (in months). We used attribute weights to gain insights into the model, highlighting features with a significant impact on predictions and offering valuable insights into the model’s behavior and decision-making process. Results The gradient boosting trees algorithm achieved an 81.55% accuracy in the classification model, while the random forest algorithm excelled in the regression model, with a root mean square error of 22.432. Notably, radiation doses around the affected areas significantly influenced survival duration. Conclusions Machine learning demonstrated the ability to provide high-accuracy predictions of survival periods in both classification and regression problems. This suggests its potential use as a decision-support tool in the process of treatment planning and resource allocation for each patient. |
| first_indexed | 2024-03-08T00:47:25Z |
| format | Article |
| id | doaj.art-bcadc5fe4d254ad6a8abd18e387108ae |
| institution | Directory Open Access Journal |
| issn | 2093-3681 2093-369X |
| language | English |
| last_indexed | 2024-03-08T00:47:25Z |
| publishDate | 2024-01-01 |
| publisher | The Korean Society of Medical Informatics |
| record_format | Article |
| series | Healthcare Informatics Research |
| spelling | doaj.art-bcadc5fe4d254ad6a8abd18e387108ae2024-02-15T06:02:59ZengThe Korean Society of Medical InformaticsHealthcare Informatics Research2093-36812093-369X2024-01-01301607210.4258/hir.2024.30.1.601192Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning TechniquesIntorn Chanudom0Ekkasit Tharavichitkul1Wimalin Laosiritaworn2 Master’s Degree Program in Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, ThailandObjectives The objective of this research is to apply machine learning (ML) algorithms to predict the survival of cervical cancer patients. The aim was to address the limitations of traditional statistical methods, which often fail to provide accurate answers due to the complexity of the problem. Methods This research employed visualization techniques for initial data understanding. Subsequently, ML algorithms were used to develop both classification and regression models for survival prediction. In the classification models, we trained the algorithms to predict the time interval between the initial diagnosis and the patient’s death. The intervals were categorized as “<6 months,” “6 months to 3 years,” “3 years to 5 years,” and “>5 years.” The regression model aimed to predict survival time (in months). We used attribute weights to gain insights into the model, highlighting features with a significant impact on predictions and offering valuable insights into the model’s behavior and decision-making process. Results The gradient boosting trees algorithm achieved an 81.55% accuracy in the classification model, while the random forest algorithm excelled in the regression model, with a root mean square error of 22.432. Notably, radiation doses around the affected areas significantly influenced survival duration. Conclusions Machine learning demonstrated the ability to provide high-accuracy predictions of survival periods in both classification and regression problems. This suggests its potential use as a decision-support tool in the process of treatment planning and resource allocation for each patient.http://e-hir.org/upload/pdf/hir-2024-30-1-60.pdfmachine learningdata visualizationuterine cervical neoplasmssurvival ratedisease attributes |
| spellingShingle | Intorn Chanudom Ekkasit Tharavichitkul Wimalin Laosiritaworn Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques Healthcare Informatics Research machine learning data visualization uterine cervical neoplasms survival rate disease attributes |
| title | Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques |
| title_full | Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques |
| title_fullStr | Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques |
| title_full_unstemmed | Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques |
| title_short | Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques |
| title_sort | prediction of cervical cancer patients survival period with machine learning techniques |
| topic | machine learning data visualization uterine cervical neoplasms survival rate disease attributes |
| url | http://e-hir.org/upload/pdf/hir-2024-30-1-60.pdf |
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