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Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches.

Md Martuza Ahamad1, Sakifa Aktar1, Md Jamal Uddin1

  • 1Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh.

Journal of Personalized Medicine
|July 27, 2022
PubMed
Summary

Early ovarian cancer detection is crucial for survival. Machine learning models accurately identified malignant cases using blood biomarkers with 91% accuracy, offering a promising diagnostic tool.

Keywords:
benign ovarian tumorsmachine learningovarian cancerstatistical analysistumor marker

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Area of Science:

  • Oncology
  • Biomedical Informatics
  • Clinical Diagnostics

Background:

  • Ovarian cancer is a leading cause of cancer death in women, lacking curative therapies.
  • Early-stage detection significantly improves patient survival rates.
  • Current diagnostic methods have limitations in early and accurate identification.

Purpose of the Study:

  • To apply machine learning and statistical methods for early ovarian cancer diagnosis.
  • To identify significant blood biomarkers for distinguishing benign from malignant cases.
  • To develop predictive models for classifying ovarian cancer patients.

Main Methods:

  • Statistical analysis including Student's t-test and log fold changes.
  • Application of machine learning models: Random Forest, SVM, DT, XGBoost, LR, GBM, LGBM.
  • Analysis of clinical data from 349 patients, focusing on serum and blood chemistry tests.

Main Results:

  • Identified key biomarkers: carbohydrate antigen 125, carbohydrate antigen 19-9, CEA, HE4, neutrophil ratio, thrombocytocrit, hematocrit, ALT, calcium, bilirubin, uric acid, and sodium.
  • Machine learning models achieved up to 91% accuracy in classifying malignant vs. benign ovarian cancer.
  • Both statistical and machine learning approaches highlighted significant diagnostic markers.

Conclusions:

  • Machine learning models show high potential for accurate early ovarian cancer detection.
  • Identified biomarkers can aid in developing more effective diagnostic strategies.
  • This approach could significantly improve patient outcomes by enabling earlier diagnosis.