Brief summary on health informatics
Abstract. This paper provides a brief summary on health informatics.
Summary
According to the human heart study at Franklin Institute the hardest part of an exercise routine is getting
started and establishing a regular pattern; our work addresses similar issues using the concept of
association rule of data mining to regulate the physical activity and exercising patterns.
Association rules have been discussed quite extensively in the data mining literature and issues related
to the efficient generation of such rules from large complex dataset have been addressed. Primarily, the
objective of the association rule of data mining is to discover the intrigue relationships among the items in
complex, and large structured or unstructured multidimensional datasets. Generally, association rules are
the data mining strategies that uncover the hidden relationship between entities in large datasets that
assists in better learning about that data, specifically in customer buying patterns in numerous business
domains. Let us consider two hypothetical examples to briefly illustrate the concept. In a supermarket, in
the entire day processing, there may be several transactions committed. Each transaction consists of the
name of the items purchased. If bread, milk, and cheese, for example, together are the common items in
most of the transactions, then this set {bread, milk, cheese} is termed as frequent set. So, a frequent set F
can be defined as the set of items (zero or more) bought together in at least in T transactions, a userdefined threshold. Then, it is most likely that these three items should be kept close inside the business
venue, presumably, resulting in product sale increase.
As a prediction analytics technique, the concept of association rule of data mining has found
applications in many areas of decision making such as cross-marketing, catalog-design, store layout and
buying patterns, etc. It represents knowledge embedded in large datasets as probabilistic implications and
intimately associated unit computation of frequent item sets or discovering frequent sets in data. In
addition to the many applications of association rule in mining categorical data, other studies in medical
diagnostics and healthcare analytics have also been reported. For example, the association rule techniques
have been proposed for image analysis. This concept, particularly in the form of the market basket
analysis has attained significant success in gaining the consumer insights in data warehouse, but its
effectiveness is still restricted to the analysis of commercial items only, housed in business facilities to
improve the product marketing and to increase the revenues. In this paper, we attempt to further expand
the bandwidth of the usage of the association rule concept beyond the commercial domains and focus on
its potential application to the public health concerns and implications of physical activity; and this
distinguish effort contributes to the novelty of this paper. To illustrate the concept, let us consider that
exercise and sleep events are two items in the frequent set. Analogous to the business domain, where the
association rule suggests keeping the associated items together to increase sales, the mild exercise event
can be associated to sleep event. As sleep event is a regularly occurring event, exercise, the sporadic event,
can also be associated with it, which could turn into a regular event. In other words, being the items in the
frequent item set, if a sleep event occurs, there is a very high priority that exercise event occurs as well.
The central focus of this study is enhancing the regularity in exercise occurrences using the concept of
association rule concept and the spatial aspect- the close proximity of the exercising equipment to the bed
- and the temporal aspect- the time to execute the exercise with respect to the sleeping time - have
important role in it. Though, the type of the exercise is an open choice. Once the exercise is over, the
weariness caused, may further help the subject to fall asleep quickly. This study does not intent to disrupt
the sleeping patterns rather to encourage the regularity in performing the mild exercise, just before sleep
that may help deepening the sleeping patterns.
Conclusion. This paper was a one pager on health informatics.
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