Survival of cervical cancer patients in Brunei Darussalam: 2002–2017

Lee, Shirley; Rahim, Nurlaylasahira Abdul; Ong, Sok King; Rahman, Hanif Abdul; Naing, Lin

doi:10.1016/j.heliyon.2023.e16080

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Additionally, there are several risk factors, including early age at first marriage, multiple pregnancies, multiple sexual partners, poor nutrition, inadequate genital hygiene, and infection with human papillomavirus (HPV). In particular, HPV types 16 and 18 are high-risk types that are the main causes of CC [ 5 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques

Chanudom,

Tharavichitkul,

Laosiritaworn

2024

Healthc Inform Res

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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.

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Section: Introductionmentioning

confidence: 99%

Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques

Chanudom,

Tharavichitkul,

Laosiritaworn

2024

Healthc Inform Res

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Cause-specific survival analysis of gynecological cancers among non-White population: a SEER based study

Priyadarshini,

Swain,

Padhee

et al. 2024

American Journal of Epidemiology

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Gynaecological cancers are the most prevalent cancers in women, making them a major public health concern for decades. Health disparities and inequalities in access to care among different racial groups have been a major concern in the US healthcare system. This study was aimed at investigating cause-specific survival rates among non-white women with gynaecological cancer and to identify risk factors associated with gynaecological cancer mortality by race. The Kaplan-Meier method was used to calculate 5-year survival estimates and various risk factors for gynaecological cancer among non-white women were analysed using Cox proportional hazard model. The findings of this study highlight the need for targeted interventions to improve access to care and reduce health disparities for non-white women with gynaecological cancer.

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Survival of cervical cancer patients in Brunei Darussalam: 2002–2017

Cited by 2 publications

References 22 publications

Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques

Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques

Cause-specific survival analysis of gynecological cancers among non-White population: a SEER based study

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