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York Institute for Health Research
Lecture Series
Health Risk Assessment for Chronic Disease
Prevention and Management
Predictive Modelling of Health Clusters for Chronic Disease Management
Yawen Xu, York University
Ted Hains & Zhen Mei, Manifold Data Mining Inc.
Chronic diseases are an increasing global challenge and their impacts increase income
inequalities and health spending (67% of health budget and 8% of GDP); deplete
household health and represent a major fiscal risk. In Canada, 3 out of 5 Canadian
older than 20 have a chronic disease and 4 of 5 are at risk.
Extensive research has described the pathway of chronic diseases:
Behavioural Risk
 Tobacco
 Diet
 Inactivity
 Alcohol
 High blood pressure
 Obesity
 Raised glucose
 Raised lipids
While this research has provided policy and guidance in supporting the key issues of
the underlying diseases, the actual determinants of health and related quality of life
remain a multi-dimensional challenge. This important theme has become a pressing
priority for WHO and CDC.
We have been pursuing the following questions in our research project:
What are the driving factors of consumer lifestyle and behaviour for “successful”
chronic disease prevention and management, and an improving quality of
How can we transform and model the metrics of the social determinants of health
into easy to use tools that promote improved chronic-care outcomes?
In this talk we’ll discuss health clusters of patients with chronic diseases, particularly
diabetes and heart diseases. We’ll show that the patients are typically grouped into
seven distinct clusters: Depressed, Gloomy, Anxious, Satisfied, Rejuvenated, Happy
and Optimistic. Furthermore, we’ll introduce a predictive modelling technique for
classifying a patient into one of these clusters, based on their demographics, mental
health and stress, health outcomes, social connection, motivation and lifestyles. We’ll
illustrate potential application of the health clusters and predictive models.
A Behavior Recommendation System for Happiness Improvement using Contrast
Pattern Mining and k-Nearest Neighbours Algorithms
Jason Chen
Joy Choi
The pursuit of happiness is sometimes a life-long goal for human beings. However,
some of the factors affecting how people feel are not apparent. More importantly,
factors tend to be related to each other: one factor may reduce or enhance the impact of
another. In order to distinguish the underlying differences between optimism and
pessimism, we use contrast pattern mining on a data set that describes a large group of
people with heart disease or diabetes to identify groups of behaviour factors that may
change people's feeling from unhappy to happy or vice versa. With such information, we
are able to build a behavior recommendation system that uses a k-nearest neighbors
based algorithm to generate recommendations to adopt or avoid contrasting groups of
behaviors. In our web-based recommendation system, upon completion of a
questionnaire by a user, the k-nearest neighbors algorithm will be used to identify
people that are similar to the user. Subsequently, global contrast patterns (i.e., the
contrast patterns found over the whole data set) that match the identified similar people
are used to find personalized behaviour recommendations that will have positive
impacts on the user's feeling.