  Test data measured by medical instruments often carry imprecise ranges that
include the true values. The latter are not obtainable in virtually all cases.
Most learning algorithms, however, carry out arithmetical calculations that are
subject to uncertain influence in both the learning process to obtain models
and applications of the learned models in, e.g. prediction. In this paper, we
initiate a study on the impact of imprecision on prediction results in a
healthcare application where a pre-trained model is used to predict future
state of hyperthyroidism for patients. We formulate a model for data
imprecisions. Using parameters to control the degree of imprecision, imprecise
samples for comparison experiments can be generated using this model. Further,
a group of measures are defined to evaluate the different impacts
quantitatively. More specifically, the statistics to measure the inconsistent
prediction for individual patients are defined. We perform experimental
evaluations to compare prediction results based on the data from the original
dataset and the corresponding ones generated from the proposed precision model
using the long-short-term memories (LSTM) network. The results against a real
world hyperthyroidism dataset provide insights into how small imprecisions can
cause large ranges of predicted results, which could cause mis-labeling and
inappropriate actions (treatments or no treatments) for individual patients.
