# Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication

arXiv: https://arxiv.org/abs/2201.03029 · HTML: https://mlsanity.com/paper/2201.03029/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 9 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Linear and Quadratic Discriminant Analysis: Tutorial](https://mlsanity.com/paper/1906.02590/) (2019)
- [Succinct Differentiation of Disparate Boosting Ensemble Learning Methods for Prognostication of Polycystic Ovary Syndrome Diagnosis](https://mlsanity.com/paper/2201.00418/) (2022)

## Built on by (most cited first, 9 of 9)

- [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)
- [Effects of Parametric and Non-Parametric Methods on High Dimensional Sparse Matrix Representations](https://mlsanity.com/paper/2202.02894/) (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)
- [Prediction of Maneuvering Status for Aerial Vehicles using Supervised Learning Methods](https://mlsanity.com/paper/2206.10303/) (2022)
- [Performance Comparison of Simple Transformer and Res-CNN-BiLSTM for Cyberbullying Classification](https://mlsanity.com/paper/2206.02206/) (2022)
- [Metric Effects based on Fluctuations in values of k in Nearest Neighbor Regressor](https://mlsanity.com/paper/2208.11540/) (2022)
- [Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification](https://mlsanity.com/paper/2303.14401/) (2023)

## Abstract (arXiv)

A lot of prognostication methodologies have been formulated for early detection of Polycystic Ovary Syndrome also known as PCOS using Machine Learning. PCOS is a binary classification problem. Dimensionality Reduction methods impact the performance of Machine Learning to a greater extent and using a Supervised Dimensionality Reduction method can give us a new edge to tackle this problem. In this paper we present Discriminant Analysis in different dimensions with Linear and Quadratic form for binary classification along with metrics. We were able to achieve good accuracy and less variation with Discriminant Analysis as compared to many commonly used classification algorithms with training accuracy reaching 97.37% and testing accuracy of 95.92% using Quadratic Discriminant Analysis. Paper also gives the analysis of data with visualizations for deeper understanding of problem.