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Evaluation of Hepatic Glucose Production in a Polycystic Ovary Syndrome Mouse Model
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Optimized polycystic ovarian disease prognosis and classification using AI based computational approaches on

Kogilavani Shanmugavadivel1, Murali Dhar M S2, Mahesh T R3

  • 1Department of Artificial Intelligence, Kongu Engineering College, Erode, India.

BMC Medical Informatics and Decision Making
|October 1, 2024
PubMed
Summary

Early diagnosis of Polycystic Ovary Syndrome (PCOS) is crucial. Machine learning models using clinical data achieved 94.44% accuracy, while VGG16 deep learning analysis of ultrasound images reached 98.29% accuracy for PCOS detection.

Keywords:
CNNClinical featuresDeep learningMachine learningPolycystic ovary syndromeTransfer learningUltrasound imagesVGG16

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Area of Science:

  • Endocrinology
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting women of reproductive age.
  • PCOS is a significant cause of infertility and associated with increased risks of miscarriage, cardiac issues, diabetes, and uterine cancer.
  • Current diagnostic methods for PCOS are time-consuming, costly, and complicated due to varied symptom presentations.

Purpose of the Study:

  • To develop and evaluate machine learning models for the early diagnosis and classification of Polycystic Ovary Syndrome (PCOS).
  • To analyze clinical features and ultrasound images for improved PCOS detection accuracy.
  • To reduce diagnostic challenges and financial burdens associated with PCOS identification.

Main Methods:

  • Clinical dataset analysis: Feature extraction using correlation-based methods reduced 45 features to 17, applied to Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) models.
  • Ultrasound image analysis: Convolutional Neural Network (CNN) and VGG16 transfer learning algorithms were employed to analyze 3856 ultrasound images.
  • Performance evaluation: Metrics included accuracy, precision, recall, F1-score for clinical data models and training/validation accuracy/loss for image analysis models.

Main Results:

  • The Support Vector Machine (SVM) model demonstrated high performance in classifying PCOS from clinical data, achieving an accuracy of 94.44%.
  • The VGG16 transfer learning model significantly outperformed the CNN model in analyzing ultrasound images, achieving a validation accuracy of 98.29%.
  • Both clinical data analysis and ultrasound image analysis showed promising results for early PCOS detection.

Conclusions:

  • Machine learning approaches, particularly SVM for clinical data and VGG16 for ultrasound imaging, offer effective tools for the early and accurate diagnosis of PCOS.
  • These computational methods can potentially streamline the diagnostic process, reduce costs, and improve patient outcomes by enabling timely intervention.
  • Further research and validation of these models are warranted for clinical implementation in PCOS management.