DOI: 10.4018/IJHISI.2018010104
International Journal of Healthcare Information Systems and Informatics
Volume 13 • Issue 1 • January-March 2018
Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
45
Data Visualization with IBM
Watson Analytics for Global
Cancer Trends Comparison from
World Health Organization
Kelvin K. F. Tsoi, Stanley Ho Big Data Decision Analytics Research Centre and Jockey Club School of Public Health and
Primary Care, Chinese University of Hong Kong, Hong Kong, China
Felix C. H. Chan, Stanley Ho Big Data Decision Analytics Research Centre, Chinese University of Hong Kong, Hong
Kong, China
Hoyee W. Hirai, Stanley Ho Big Data Decision Analytics Research Centre, Chinese University of Hong Kong, Hong Kong,
China
Gary K. S. Keung, Stanley Ho Big Data Decision Analytics Research Centre, Chinese University of Hong Kong, Hong
Kong, China
Yong-Hong Kuo, Stanley Ho Big Data Decision Analytics Research Centre, Chinese University of Hong Kong, Hong
Kong, China
Samson Tai, IBM China/Hong Kong Limited, Hong Kong, China
Helen M. L. Meng, Stanley Ho Big Data Decision Analytics Research Centre and Department of Systems Engineering and
Engineering Management, Chinese University of Hong Kong, Hong Kong, China
ABSTRACT
Visual analytics is widely used to explore data patterns and trends. This work leverages cancer data
collected by World Health Organization (WHO) across a hundred of cancer registries worldwide.
In this study, the authors present a visual analytics platform, IBM Watson Analytics, to explore the
patterns of global cancer incidence. They included 26 forms of cancers from eight different geographic
regions which are United States, the United Kingdom, Costa Rica, Sweden, Croatia, Japan, Hong
Kong and China (Shanghai). An interactive interface was applied to plot a choropleth map to show
global cancer distribution, and line charts to demonstrate historical cancer trends over 29 years.
Subgroup analyses were conducted for different age groups. With real-time interactive features, one
can easily explore the data with a selection of any cancer type, gender, age group, or geographical
region. This platform is running on the cloud, so it can handle data in huge volumes, and is accessible
by any computer connected to the Internet. IBM Watson Analytics released a latest version named
“IBM Watson Analytics New User Experience” in the end of 2016. The new version streamlined the
process to add data, discover data meaning and display result visually. The authors discuss the new
features in the end of this paper.
KeyWORDS
Cancer Incidence, Choropleth Map, Global Comparison, Interactive Interface, Visual Analytics
International Journal of Healthcare Information Systems and Informatics
Volume 13 • Issue 1 • January-March 2018
46
1. INTRODUCTION
Visual analytics have been shown to be effective for data exploration (Keim, 2002), but the requirement
of computational power is high for global comparisons of disease trends. Cloud computing is a model
for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable
computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly
provisioned and released with minimal management effort or service provider interaction. To
the consumer, the capabilities available for provisioning often appear to be unlimited and can be
appropriated in any quantity at any time (Mell & Grance, 2011). The elasticity of cloud computing
meets the demand of high computational power for data exploration. Therefore, cloud computing is
one of major enablers for explorations of data. This is a general shift of computer processing, storage,
and software delivery away from the traditional desktop computers and local servers towards the
cloud (Saranya & Sunitha, 2012).
Cancer is one of the leading causes of morbidity and mortality worldwide, with approximately
14 million new cases and 8.2 million cancer related deaths in 2012. It is expected that annual cancer
cases will rise to 22 million within the next 2 decades. The prevalence of cancer varies by gender,
age, ethnicity, geographical location, economic status, and so on. Generally, the cancer causes of
death were common on breast, lung, liver, stomach, colon and rectum (International Agency for
Research on Cancer World Health Organization, 2014). Although the age-standardized incidence
rates on some cancer showed stable trends, but the prevalence of cancer has grown along with the
ageing population. To better understand the progression of cancer, cancer registries were set up in
different countries. The first population-based cancer registry was in Germany Hamburg in 1926
(Wagner, 1991). Cancer registries have been widely used in epidemiological research, so the World
Health Organization (WHO) formed an International Agency for Research on Cancer (IARC) to
collect cancer registry data across different countries. Descriptive studies use the registry database to
examine differences in the incidence of cancer for different patient characteristics (Parkin, 2006). The
data volume of the global cancer incidence is huge, so visual analytics can help to enhance the data
interpretation on disease distribution and trends. It simplifies complex data to intuitive and interactive
visual representation for faster and better understanding of the meaning of the data.
The main contribution in this study is to use a visual analytics platform (IBM Watson Analytics)
to aid in visualizing differences in cancer trends and patterns embedded within data sourced from
WHO cancer registries. The research aims to answer several major questions presented to us from
cancer researchers, including, but not limited to (i) What are the top-ranking forms of cancers (ii)
across different regions, (iii) across gender, (iv) over the years, (v) across high- and mid-income
regions, and (vi) across different age groups? In the following session, we provide a visualization
of the WHO data that enables us to intuitively answer these questions. In the future, such intuition
can guide us in further explorations. For example, we can use the patterns to show the effectiveness
of colorectal cancer (CRC) screening programme in US, and compare the results with some regions
that do not have screening programme.
We selected the Choropleth map and the traditional line charts for demonstration. The Choropleth
map used to present the population density in different geographical regions (MacEachren, Brewer,
& Pickle, 1998). In this study, the regions with high volume of cancer incidence will appear with
darker colors on the map. The line chart is the traditional way of presentation which shows data trends
along timeline. In this study, line charts are used to demonstrate cancer trends across regions and
different population groups, such as for different gender. A matrix of line charts is also developed
for cross-sectional comparison between different age groups across the regions.
The structure of this paper is organized as follows. Section 2 discusses related work in the
academic field. Section 3 focuses on data structure of cancer registry from WHO, while Section
4 highlights data categorization for subgroup comparison. Section 5 provides details on the visual
analytics environment, while Section 6 presents the application scenario. Section 7 discusses some
new features in IBM Watson Analytics New User Experience, the latest release of IBM Watson
Analytics. Finally, Section 8 provides a conclusion with thoughts for future research.
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