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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.

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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.