# Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification

arXiv: https://arxiv.org/abs/2303.14401 · HTML: https://mlsanity.com/paper/2303.14401/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 25 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Deep Linear Discriminant Analysis](https://mlsanity.com/paper/1511.04707/) (2015)
- [Succinct Differentiation of Disparate Boosting Ensemble Learning Methods for Prognostication of Polycystic Ovary Syndrome Diagnosis](https://mlsanity.com/paper/2201.00418/) (2022)
- [Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication](https://mlsanity.com/paper/2201.03029/) (2022)
- [Binary Classification for High Dimensional Data using Supervised Non-Parametric Ensemble Method](https://mlsanity.com/paper/2202.07779/) (2022)
- [Combining Varied Learners for Binary Classification using Stacked Generalization](https://mlsanity.com/paper/2202.08910/) (2022)
- [Detection of Tool based Edited Images from Error Level Analysis and Convolutional Neural Network](https://mlsanity.com/paper/2204.09075/) (2022)
- [Residual-Concatenate Neural Network with Deep Regularization Layers for Binary Classification](https://mlsanity.com/paper/2205.12775/) (2022)

## Abstract (arXiv)

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.