Data Informed Decision Making

Data Informed Decision Making: Cardiovascular Disease Prevention

Mohammad Ammar Faiq, Ammar (TU Delft Technology, Policy and Management)

Cunningham, Scott (mentor)
van der Voort, Haiko (mentor)
Kist, Janet (graduation committee)
Groenwold, Rolf (graduation committee)

Delft University of Technology

Engineering and Policy Analysis

2019-08-29

Cardiovascular diseases are considered as one the deadliest disease and have also been the most prominent health burden around the world and particularly in the Netherlands. Enormously mitigation has been done to reduce the death burden, by improving the quality of health care services and research related to cardiovascular diseases. One prominent strategy to reduce it is to identify early symptoms of cardiovascular diseases among the potential population. Currently, the prevailing cardiovascular disease risk prediction guidelines that used by a general practitioner only taking into account straightforward factors into their risk factors, and significant improvement to the guidelines is needed to include more socio-economic factors into account since many expert realize the fallacy of the systems. This research expands the current cardiovascular risk estimation guidelines with socio-economic factors such as ethnicity, occupation, social deprivation, by utilizing Bayesian network modeling to understand better the nature of socio-economic factors related to cardiovascular disease risk among the Hague population in the Netherlands. This research is collaborative research between Leiden University of Medical Center (LUMC) as the problem owner, the data provider and knowledge expert and TU Delft as an analyst.

Cardiovascular disease
Bayesian Network
Survival analysis
Street-level bureaucrats
CRISP-DM

http://resolver.tudelft.nl/uuid:97c50f56-bdec-4da2-af17-27687666d1d8

2019-10-31

Student theses

master thesis

© 2019 Ammar Mohammad Ammar Faiq