Causal effect of age first had sexual intercourse and lifetime number of sexual partners on cervical cancer

Zhou, Yuan-yuan; Chang, Man; Li, Chuan-ping; Han, Xi-ling; Fang, Ping; Xia, Xiao-ping

doi:10.1016/j.heliyon.2023.e23758

Cited by 1 publication

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An analysis of the data indicated that women aged 40 to 72 were at an increased risk of CC, which is consistent with previous research that showed a higher prevalence in the 40–60 age group compared to younger women. The observed tumor sizes ranging from 4 to 7 cm are in agreement with similar findings reported in other studies [ 23 , 24 ]. Despite variations in variables due to differences in inclusion/ exclusion criteria and data collection methods, our dataset is consistent with those from other studies.…”

Section: Discussionsupporting

confidence: 93%

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

Chanudom,

Tharavichitkul,

Laosiritaworn

2024

Healthc Inform Res

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 93%