The investigation of mercury presence in human blood: an extrapolation from animal data using neural networks

Abstract

In this research effort a neural network approach was used as a method of extrapolating the presence of mercury in human blood from animal data. We also investigated the effect of different data representations (As-is, Category, Simple binary, Thermometer, and Flag) on the model performance. In addition, we used the Rough Sets methodology to identify the redundant independent variables and then examined the proposed extrapolation model performance for a reduced set of independent variables. Moreover, a quality measure was introduced that revealed that the proposed extrapolation model performed extremely well for the Thermometer data representation.

Data Mining and Neural Networks: The Impact of Data Representation

“…In the task of prediction, one particular predictive model might give the best result for one data set but gives the poor results in another data set although these two data sets contain the same data with different representations [14], [15], [16], [17].…”

“…The methodology for this research is being adapted from [14] by using different data representations on the data set, and the steps involved in carrying out the studies are shown in Figure 1 [14]. The study starts with data collection, followed by data preparation stage, analysis and experiment stage, and finally, investigation and comparison stage.…”

“…In data representation study, [14] used NNs to extrapolate the presence of mercury in human blood from animal data. The effect of different data representations such as As-is, Category, Simple binary, Thermometer, and Flag on the prediction models are investigated.…”

Identification of Core, Semi-Core and Redundant Attributes of a Dataset

“…Although several approaches are available for imputing missing data [7,8,9] but we have decided to remove those records with missing data. The decision stemmed from the fact that any imputing approach may introduce a bias to the dataset.…”