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<p>Matching is a research and application domain within machine learning and statistics that provides tools to aggregate, combine, or compare data from different sources, tables, or datasets based on common criteria. This work aims to investigate data matching process applied to Polycystic Ovary Syndrome datasets using low-cost information. Here, we highlight the importance of considering different strategies for data fusion based on statistical tests in order to determine which variables should be integrated into the final dataset. Moreover, we are evaluating how different matching strategies affect the performance of the data-learning models for classifying PCOS. We conduct the experimental evaluation using real data to illustrate the effectiveness of the proposed approach. The performance of various classifiers is measured by different metrics and the results are also discussed in comparison with those corresponding to machine learning literature related to PCOS data.</p>

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Predicting polycystic ovary syndrome using statistical data integration and machine learning with low-cost information

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月4日

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  • Giullia B. de A. Maranhão,
  • Wanessa W. L. Freitas,
  • Beatriz A. de Miranda,
  • Camila S. Lins,
  • Renata M. C. R. de Souza

摘要

Matching is a research and application domain within machine learning and statistics that provides tools to aggregate, combine, or compare data from different sources, tables, or datasets based on common criteria. This work aims to investigate data matching process applied to Polycystic Ovary Syndrome datasets using low-cost information. Here, we highlight the importance of considering different strategies for data fusion based on statistical tests in order to determine which variables should be integrated into the final dataset. Moreover, we are evaluating how different matching strategies affect the performance of the data-learning models for classifying PCOS. We conduct the experimental evaluation using real data to illustrate the effectiveness of the proposed approach. The performance of various classifiers is measured by different metrics and the results are also discussed in comparison with those corresponding to machine learning literature related to PCOS data.