Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification
This work addresses PCOS diagnosis, a domain-specific medical problem, with an incremental improvement in classification.
The paper tackles PCOS classification by proposing a deep learning variation of Linear Discriminant Analysis (Deep LDA), achieving improved performance over traditional methods.
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.