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Updated: Sep 20, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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A Model for Predicting Cervical Cancer Using Machine Learning Algorithms.

Naif Al Mudawi1, Abdulwahab Alazeb1

  • 1Department of Computer Science, College of Computer Science and Information System, Najran University, Najran 55461, Saudi Arabia.

Sensors (Basel, Switzerland)
|June 10, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict cervical cancer early stages. Random Forest and Decision Tree algorithms achieved 100% accuracy, offering a promising tool for early disease detection and prevention.

Keywords:
cervical cancergradient boostinghuman papillomavirus (HPV)machine learning (ML)support vector machine (SVM)

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Area of Science:

  • Computational biology
  • Medical informatics
  • Oncology

Background:

  • Machine learning (ML) and deep learning (DL) are increasingly used for analyzing large datasets and generating insights in healthcare.
  • Early detection of serious illnesses like cancer is crucial for effective prevention and treatment.
  • Cervical cancer remains a significant health concern for women globally, highlighting the need for improved diagnostic methods.

Purpose of the Study:

  • To develop and evaluate machine learning algorithms for the early prediction of cervical cancer.
  • To compare the performance of various classic ML algorithms in classifying cervical cancer.
  • To assess the computational complexity and efficacy of the predictive models.

Main Methods:

  • A research dataset was utilized, involving data pre-processing and predictive model selection (PMS).
  • Experiments were conducted using Decision Tree (DT), Logistic Regression (LR), Support Vector Machine (SVM), K-nearest Neighbors (KNN), Adaptive Boosting, Gradient Boosting, Random Forest (RF), and XGBoost algorithms.
  • Computational complexity of ML techniques was analyzed to evaluate model efficiency.

Main Results:

  • Random Forest, Decision Tree, Adaptive Boosting, and Gradient Boosting algorithms achieved a 100% classification score for cervical cancer prediction.
  • Support Vector Machine (SVM) demonstrated 99% accuracy.
  • The study also included a survey of 132 Saudi Arabian volunteers regarding computer-assisted prediction and human papillomavirus (HPV).

Conclusions:

  • Machine learning algorithms, particularly Random Forest, Decision Tree, Adaptive Boosting, and Gradient Boosting, show exceptional accuracy in predicting cervical cancer.
  • These ML models offer a powerful and efficient approach for early cervical cancer detection, potentially improving patient outcomes.
  • Further research and integration of these tools could significantly enhance preventative healthcare strategies for cervical cancer.