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Ensemble Learning for Data-Driven Diagnosis of Polycystic Ovary Syndrome

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Intelligent Systems Design and Applications (ISDA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 418))

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Abstract

The emphasis of this article is on the data-driven diagnosis of polycystic ovary syndrome (PCOS) in women. Data from the Kaggle repository is used to train ensemble machine learning algorithms. There are 177 women with PCOS in this dataset, which includes 43 different characteristics. To begin, a univariate feature selection and feature elimination method are used to identify the most accurate characteristics for predicting PCOS. The characteristics are ranked, and the ratio of Follicle-stimulating hormone (FSH) to Luteinizing hormone (LH) is determined to be the most significant one. Cross-validation method is applied while the feature selection and feature elimination are occurring. Voting hard, voting soft and CatBoost are among the classifiers used on the dataset. According to the findings, the top 13 most significant risk factors accurately predict the onset of PCOS. With the use of 5, 10, 20-fold cross-validation on ensemble learning’s 13 most critical characteristics, results show that soft voting has the highest accuracy of 91.12%. As a result, ensemble learning can be used to accurately identify PCOS patients.

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Author information

Authors and Affiliations

  1. Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, 1205, Dhaka, Bangladesh

    Subrato Bharati, Prajoy Podder & M. Rubaiyat Hossain Mondal

  2. Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, 45221, USA

    V. B. Surya Prasath

  3. Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 45229, USA

    V. B. Surya Prasath

  4. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, 45257, USA

    V. B. Surya Prasath

  5. Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, 45267, USA

    V. B. Surya Prasath

  6. Machine Intelligence Research Labs (MIR Labs), Auburn, USA

    Niketa Gandhi

Authors
  1. Subrato Bharati
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  2. Prajoy Podder
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  3. M. Rubaiyat Hossain Mondal
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  4. V. B. Surya Prasath
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  5. Niketa Gandhi
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Corresponding author

Correspondence to Subrato Bharati .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  3. Institut für Wirtschaftsinformatik, Fachhochschule Nordwestschweiz, Olten, Switzerland

    Thomas Hanne

  4. Department of Computer Science and information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Federal University of Bahia, Ondina, Brazil

    Tatiane Nogueira Rios

  6. Nantong University, Nantong Shi, Jiangsu, China

    Weiping Ding

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Bharati, S., Podder, P., Mondal, M.R.H., Surya Prasath, V.B., Gandhi, N. (2022). Ensemble Learning for Data-Driven Diagnosis of Polycystic Ovary Syndrome. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_116

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