Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication

• URL: http://arxiv.org/abs/2201.03029v1
• Date: Sun, 9 Jan 2022 14:55:17 GMT
• Title: Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication
• Authors: Abhishek Gupta, Himanshu Soni, Raunak Joshi, Ronald Melwin Laban
• Abstract summary: Polycystic Ovary Syndrome also known as PCOS is a binary classification problem. 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.
• Score: 3.1871776847712523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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