  Currently, many researchers and analysts are working toward medical diagnosis
enhancement for various diseases. Heart disease is one of the common diseases
that can be considered a significant cause of mortality worldwide. Early
detection of heart disease significantly helps in reducing the risk of heart
failure. Consequently, the Centers for Disease Control and Prevention (CDC)
conducts a health-related telephone survey yearly from over 400,000
participants. However, several concerns arise regarding the reliability of the
data in predicting heart disease and whether all of the survey questions are
strongly related. This study aims to utilize several machine learning
techniques, such as support vector machines and logistic regression, to
investigate the accuracy of the CDC's heart disease survey in the United
States. Furthermore, we use various feature selection methods to identify the
most relevant subset of questions that can be utilized to forecast heart
conditions. To reach a robust conclusion, we perform stability analysis by
randomly sampling the data 300 times. The experimental results show that the
survey data can be useful up to 80% in terms of predicting heart disease, which
significantly improves the diagnostic process before bloodwork and tests. In
addition, the amount of time spent conducting the survey can be reduced by 77%
while maintaining the same level of performance.
