of 909 records with 15 medical attributes (factors) were obtained
from the Cleveland Heart Disease database. Figure 1 lists the
attributes. The records were split equally into two datasets:
training dataset (455 records) and testing dataset (454 records)
Table 2 summarizes the results of all three models. Naïve Bayes
appears to be most effective as it has the highest percentage of
correct predictions (86.53%) for patients with heart disease,
followed by Neural Network (with a difference of less than 1%)
and Decision Trees. Decision Trees, however, appears to be most
effective for predicting patients with no heart disease (89%)
compared to the other two models.