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  11. Prediction Of Ovarian Cancer Using Artificial Intelligence Tools

Prediction of ovarian cancer using artificial intelligence tools

Seyed Mohammad Ayyoubzadeh1,2, Marjan Ahmadi3, Alireza Banaye Yazdipour1,4,5

  • 1Department of Health Information Management, School of Allied Medical Sciences Tehran University of Medical Sciences Tehran Iran.

Health Science Reports
|July 1, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence models accurately predict ovarian cancer using tumor markers. The random forest model demonstrated the highest accuracy, aiding in early and cost-effective diagnosis for women.

Area of Science:

  • Oncology
  • Biomedical Informatics
  • Machine Learning

Background:

  • Ovarian cancer is a significant cause of mortality in women.
  • Accurate and rapid prediction of ovarian tumors is critical for timely intervention.
  • Artificial intelligence (AI) offers a promising approach for developing precise diagnostic tools.

Purpose of the Study:

  • To develop and evaluate AI models for predicting ovarian cancer.
  • To identify key predictive factors for ovarian tumors.
  • To compare the performance of different AI algorithms in ovarian cancer prediction.

Main Methods:

  • Analysis of a dataset comprising 171 benign and 178 malignant ovarian tumor cases.
  • Preprocessing of blood test results and tumor marker data, including outlier removal and imputation.
Keywords:
artificial intelligencemachine learningovarian cancer

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  • Feature selection using information gain and Gini index.
  • Development of predictive models using Random Forest (RF), Support Vector Machine (SVM), Decision Trees (DT), and Artificial Neural Network (ANN).
  • Performance evaluation via 10-fold cross-validation, assessing specificity, sensitivity, accuracy, and Area Under the ROC Curve (AUC).

    • Main Results:

    • Key predictive factors identified include HE4, CA125, and NEU.
    • The Random Forest (RF) model achieved the highest accuracy, exceeding 86%.
    • Other models showed strong performance: SVM (85.25%), DT (82.91%), and ANN (79.35%).

    Conclusions:

    • AI models, particularly RF, demonstrate high accuracy and sensitivity for ovarian cancer prediction.
    • These AI tools can facilitate earlier, simpler, and more cost-effective diagnosis.
    • Future research should explore integrating AI with imaging data and serum biomarkers for enhanced diagnostic models.