Modelling And Predicting Sudden Death Following Myocardial Infarction In Malaysia
Mvocardial infarction (MI), which also known as heart attack, has been the top cause of death worldwide. High rates of mortality were also reported within the first 30 days alter a heart attack. The mortality within a short span of' time also called as sudden death. Using logistic regression...
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| Format: | Thesis |
| Language: | English |
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| Summary: | Mvocardial infarction (MI), which also known as heart attack, has been the top cause
of death worldwide. High rates of mortality were also reported within the first 30 days
alter a heart attack. The mortality within a short span of' time also called as sudden
death. Using logistic regression, we investigate the relationship between the risk of'
sudden death following MI and various risk factors and Annulate a probability model
to predict sudden death. The dataset used in the study consist of 28.412 observations
from 19 hospitals and medical institutions, which was provided by National
Cardiovascular Disease Database (NCVD). Regression results show that year of' MI
event, gender. and smoking habit have the least impact on the probability of' sudden
death. Elderlies, diabetic, and non-hypertensive Ml patients have a higher risk of sudden
death. BMI and cholesterol level were observed to have an inverse-J-shaped
relationship with sudden death, which means people in middle classes have higher
survival chance. The overall performance of the logistic model suggests that the model
can be used to predict the outcome MI patients after 30 days of onset.
Keywords: Iogistic regression, mortaIity prediction, myocardial infraction, risk factors,
sudden death |
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