Predicting Heart Disease and Reducing Survey Time Using Machine Learning Algorithms

TL;DR

This paper explores machine learning techniques to predict heart disease from CDC survey data, achieving high accuracy and reducing survey time by 77%, thus enhancing early diagnosis and efficiency.

Contribution

It demonstrates the effectiveness of feature selection and stability analysis in improving heart disease prediction accuracy and reducing survey length.

Findings

Survey data can predict heart disease with up to 80% accuracy.

Survey time can be reduced by 77% without losing predictive performance.

Machine learning models outperform traditional diagnostic methods.

Abstract

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.