Abstract
Smart healthcare integrates an advanced wave of information technology using smart devices to collect health-related medical science data. Such data usually exist in unstructured, noisy, incomplete, and heterogeneous forms. Annotating these limitations remains an open challenge in deep learning to classify health conditions. In this paper, a long short-term memory (LSTM) based health condition prediction framework is proposed to rectify imbalanced and noisy data and transform it into a useful form to predict accurate health conditions. The imbalanced and scarce data is normalized through coding to gain consistency for accurate results using synthetic minority over sampling technique. The proposed model is optimized and fine-tuned in an end to end manner to select ideal parameters using tree parzen estimator to build a probabilistic model. The patient's medication is pigeonholed to plot the diabetic condition's risk factor through an algorithm to classify blood glucose metrics using a modern surveillance error grid method. The proposed model can efficiently train, validate, and test noisy data by obtaining consistent results around 90% over the state of the art machine and deep learning techniques and overcoming the insufficiency in training data through transfer learning. The overall results of the proposed model are further tested with secondary datasets to verify model sustainability.