Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication
This work addresses prognostication for PCOS, a specific medical condition, but is incremental as it applies existing dimensionality reduction and classification methods to this domain.
The paper tackled the problem of early detection of Polycystic Ovary Syndrome (PCOS) as a binary classification task by applying Discriminant Analysis in linear and quadratic forms, achieving a testing accuracy of 95.92% with Quadratic Discriminant Analysis.
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.