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In most cases, excessive hormone production is prevented by negative feedback—a loop that starts with a stimulus inducing the release of a particular substance, like a hormone, to maintain a certain level before triggering a signal that results in a decrease in further release of the hormone.
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The ovarian cycle regulates endometrial changes throughout a single menstrual cycle via the coordinated action of gonadotrophin-releasing hormone (GnRH) and gonadotrophins.
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The ovarian cycle is meticulously regulated by the hypothalamic-pituitary-gonadal axis. This cycle orchestrates the release of a mature oocyte, essential for reproduction.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Fuzzy machine learning logic utilization on hormonal imbalance dataset.

Rabia Khushal1, Ubaida Fatima1

  • 1Department of Mathematics, NED University of Engineering & Technology, Pakistan.

Computers in Biology and Medicine
|April 17, 2024
PubMed
Summary

A new fuzzy data transformation technique enhances PCOS diagnosis by converting binary data into a three-class system. This improved method offers a broader spectrum for detecting Polycystic Ovary Syndrome (PCOS) and enabling early preventive measures.

Keywords:
Biological datasetFuzzy logicHormonal imbalanceMachine learningPolycystic ovary syndrome (PCOS)

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Area of Science:

  • Biomedical Informatics
  • Machine Learning
  • Data Science

Background:

  • Hormonal imbalances, particularly Polycystic Ovary Syndrome (PCOS), significantly impact women's health.
  • Existing PCOS datasets often have limitations, including binary variables and limited diagnostic output.
  • This restricts the ability to accurately diagnose and manage PCOS.

Purpose of the Study:

  • To introduce a novel fuzzy data transformation technique for hormonal imbalance datasets.
  • To improve the diagnostic capabilities for Polycystic Ovary Syndrome (PCOS).
  • To enhance the classification accuracy and provide a broader diagnostic spectrum for PCOS.

Main Methods:

  • A novel fuzzy data transformation technique was developed and applied to a hormonal imbalance dataset.
  • Input variables with binary responses were transformed into a fuzzy format.
  • An adaptive fuzzy machine learning logic model was implemented for inference on the transformed dataset.

Main Results:

  • The fuzzy transformation expanded the diagnostic spectrum from binary (present/absent) to three classes, indicating potential PCOS presence.
  • Machine learning applied to the fuzzy-transformed dataset provided a more nuanced diagnosis compared to the untransformed dataset.
  • This broader spectrum allows for earlier detection and alerts patients to take preventive measures.

Conclusions:

  • The proposed fuzzy data transformation technique effectively addresses limitations in binary datasets for PCOS diagnosis.
  • This approach enhances machine learning model performance, offering a more comprehensive understanding of PCOS risk.
  • Early detection through fuzzy transformation can lead to timely interventions and improved patient outcomes.