Abstract
Heart disease is without doubt getting more and more popular in human society. According to the statistics of the World Heart Federation, one person dies of heart diseases for every 3 deaths in the world, and the number of deaths due to heart disease and stroke is as high as 17.5 million in the world every year. In this paper, 5 potential influencing factors and their data are selected to construct a logistic regression model to predict the possibility of one catching a heart disease so that early prevention may be achieved in time. During the construction of the model, some transformations are applied to the predictors to optimize the model. In the end, cross-validation method is used to test the final model, and the results show that the accuracy of the model is over 73%. In conclusion, this model can briefly predict the possibility of one catching a heart disease, and also reveal that the factors chosen do have some significant impacts on heart disease prediction.
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