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Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification

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Intelligent Computing and Networking

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 632))

Abstract

The polycystic ovary syndrome diagnosis is a problem that can be leveraged using prognostication based learning procedures. Many implementations of PCOS can be seen with Machine Learning but the algorithms have certain limitations in utilizing the processing power graphical processing units. The simple machine learning algorithms can be improved with advanced frameworks using Deep Learning. The Linear Discriminant Analysis is a linear dimensionality reduction algorithm for classification that can be boosted in terms of performance using deep learning with Deep LDA, a transformed version of the traditional LDA. In this result oriented paper we present the Deep LDA implementation with a variation for prognostication of PCOS.

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Acknowledgements

We would genuinely like to thank Mr. Prasoon Kottarathil for making the polycystic ovary syndrome dataset available through Kaggle platform. We would also like to deeply thank for the contributions of VahidooX - https://github.com/VahidooX/DeepLDA and Thomas Chaton https://github.com/tchaton/DeepLDA for providing us the basic implementations of Deep LDA based from the original paper.

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Authors and Affiliations

  1. University of Mumbai, Mumbai, 400032, India

    Raunak Joshi & Abhishek Gupta

  2. St. John College of Engineering and Management, Palghar, 401404, India

    Himanshu Soni & Ronald Laban

Authors
  1. Raunak Joshi
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  2. Abhishek Gupta
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  3. Himanshu Soni
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  4. Ronald Laban
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Corresponding author

Correspondence to Abhishek Gupta .

Editor information

Editors and Affiliations

  1. Aurel Vlaicu University, Arad, Romania

    Valentina Emilia Balas

  2. National Institute of Technology, Bhopal, Madhya Pradesh, India

    Vijay Bhaskar Semwal

  3. Thakur college of Engineering and Technology, Mumbai, India

    Anand Khandare

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

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Joshi, R., Gupta, A., Soni, H., Laban, R. (2023). Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification. In: Balas, V.E., Semwal, V.B., Khandare, A. (eds) Intelligent Computing and Networking. Lecture Notes in Networks and Systems, vol 632. Springer, Singapore. https://doi.org/10.1007/978-981-99-0071-8_3

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  • DOI: https://doi.org/10.1007/978-981-99-0071-8_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0070-1

  • Online ISBN: 978-981-99-0071-8

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