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Prediction of PCOS and PCOD in Women Using ML Algorithms

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ICT for Intelligent Systems ( ICTIS 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 361))

Abstract

Polycystic ovarian disorder (PCOD) is an endocrine disorder resulting in hormonal imbalances and the production of the androgen hormone is notably heightened. Polycystic ovary syndrome (PCOS) is an endocrine disorder which is mostly found in women after puberty. The dataset used in our research work consists of 541 patients instances with 45 attributes related to the disease. It is collected from UCI ML depository. In this paper, we employed various ensemble learning algorithm like Random Forest, Bagging classifier, AdaBoosting and Gradient Boosting. Our model inferred the prediction of PCOD in young women with the highest performance of 91.7% through Gradient Boosting having F1 score of 92%.

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Authors and Affiliations

  1. Department of Artificial Intelligence and Data Science, Global Academy of Technology, Bengaluru, India

    M. J. Lakshmi, D. S. Spandana, Harini Raj, G. Niharika, Ashwini Kodipalli, Shoaib Kamal & Trupthi Rao

Authors
  1. M. J. Lakshmi
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  2. D. S. Spandana
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  3. Harini Raj
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  4. G. Niharika
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  5. Ashwini Kodipalli
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  6. Shoaib Kamal
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  7. Trupthi Rao
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Corresponding author

Correspondence to M. J. Lakshmi .

Editor information

Editors and Affiliations

  1. Hertfordshire Business School, University of Hertfordshire, Hatfield, Hertfordshire, UK

    Jyoti Choudrie

  2. Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India

    Parikshit N. Mahalle

  3. University Putra Malaysia, Serdang, Selangor, Malaysia

    Thinagaran Perumal

  4. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Lakshmi, M.J. et al. (2023). Prediction of PCOS and PCOD in Women Using ML Algorithms. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) ICT for Intelligent Systems. ICTIS 2023. Smart Innovation, Systems and Technologies, vol 361. Springer, Singapore. https://doi.org/10.1007/978-981-99-3982-4_9

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