  Many complex Deep Learning models are used with different variations for
various prognostication tasks. The higher learning parameters not necessarily
ensure great accuracy. This can be solved by considering changes in very deep
models with many regularization based techniques. In this paper we train a deep
neural network that uses many regularization layers with residual and
concatenation process for best fit with Polycystic Ovary Syndrome Diagnosis
prognostication. The network was built with improvements from every step of
failure to meet the needs of the data and achieves an accuracy of 99.3%
seamlessly.
