Towards Big Neural Networks: Analysis of Deep Learning Techniques on Diabetes Prediction


Towards Big Neural Networks: Analysis of Deep Learning Techniques on Diabetes Prediction – The majority of tasks in artificial life (including medical data) require the prediction of individual biomarkers for the specific test (e.g. blood pressure or blood glucose) to be considered. However, even though many biomarkers are proposed, current biomarker research deals with only single test. As long as the knowledge of biomarker classification and classification is shared amongst all the test subjects, it has a much higher potential to improve the performance of our system. The best-studied (i.e. the best) biomarker classifier was the one based on genetic algorithm based on gene expression. In this paper, we propose to utilize a genetic algorithm for the purpose of developing a biomarker classifier with the potential of reducing the overall time it takes for the agent to make a decision to classify its samples.

The most successful and efficient algorithms in the literature have not seen a major increase in adoption. However, existing methods for learning linear models have limited their application to higher dimensions. Inspired by the high-dimensional domain, we propose a novel linear estimator that can be used to encode and evaluate the nonlinear information contained in high-dimensional variables. We then use the learned estimator to reconstruct the model from the information stored in the high-dimensional variable space. Our estimation method can perform better than the state-of-the-art methods in terms of accuracy and robustness.

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Towards Big Neural Networks: Analysis of Deep Learning Techniques on Diabetes Prediction

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  • Convolutional Residual Learning for 3D Human Pose Estimation in the Wild

    Bayesian Online Nonparametric Adaptive Regression Models for Multivariate Time SeriesThe most successful and efficient algorithms in the literature have not seen a major increase in adoption. However, existing methods for learning linear models have limited their application to higher dimensions. Inspired by the high-dimensional domain, we propose a novel linear estimator that can be used to encode and evaluate the nonlinear information contained in high-dimensional variables. We then use the learned estimator to reconstruct the model from the information stored in the high-dimensional variable space. Our estimation method can perform better than the state-of-the-art methods in terms of accuracy and robustness.


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