A common activity carried out by healthcare professionals is to test various hypotheses on longitudinal study data in an effort to develop new and more reliable algorithms that might determine the possibility of developing certain illnesses. The In-MINDD project provides input from a number of European dementia experts to identify the most accurate model of inter-related risk factors which can yield a personalised dementia risk quotient and profile. This model is then validated against the large population-based prospective Maastricht Aging Study (MAAS) dataset. As part of this overall goal, the research presented in this paper demonstrates how we can automate the process of mapping modifiable risk factors against large sections of the aging study and thus, use information technology to provide more powerful query interfaces.
Abstract-Clinical studies provide interesting case studies for data mining researchers, given the often high degree of dimensionality and long term nature of these studies. In areas such as dementia, accurate predictions from data scientists provide vital input into the understanding of how certain features (representing lifestyle) can predict outcomes such as dementia. Most research involved has used traditional or shallow data mining approaches which have been shown to offer varying degrees of accuracy in datasets with high dimensionality. In this research, we explore the use of deep learning architectures, as they have been shown to have high predictive capabilities in image and audio datasets. The purpose of our research is to build a framework which allows easy reconfiguration for the performance of experiments across a number of deep learning approaches. In this paper, we present our framework for a configurable deep learning machine and our evaluation and analysis of two shallow approaches: regression and multi-layer perceptron, as a platform to a deep belief network, and using a dataset created over the course of 12 years by researchers in the area of dementia.
Abstract. Recent research has found that deep learning architectures show significant improvements over traditional shallow algorithms when mining high dimensional datasets. When the choice of algorithm employed, hyper-parameter setting, number of hidden layers and nodes within a layer are combined, the identification of an optimal configuration can be a lengthy process. Our work provides a framework for building deep learning architectures via a stepwise approach, together with an evaluation methodology to quickly identify poorly performing architectural configurations. Using a dataset with high dimensionality, we illustrate how different architectures perform and how one algorithm configuration can provide input for fine-tuning more complex models.
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