Author(s)

Huating Sun¹, Jianan Pan²

¹Department of Geography, University of Washington, Seattle, USA.
²School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, Suzhou, China.

In recent years, the number of cases of heart disease has been greatly increasing, and heart disease is associated with a high mortality rate. Moreover, with the development of technologies, some advanced types of equipment were invented to help patients measure health conditions at home and predict the risks of having heart disease. The research aims to find the accuracy of self-measurable physical health indicators compared to all indicators measured by healthcare providers in predicting heart disease using five machine learning models. Five models were used to predict heart disease, including Logistics Regression, K Nearest Neighbors, Support Vector Model, Decision tree, and Random Forest. The database used for the research contains 13 types of health test results and the risks of having heart disease for 303 patients. All matrices consisted of all 13 test results, while the home matrices included 6 results that could test at home. After constructing five models for both the home matrices and all matrices, the accuracy score and false negative rate were computed for every five models. The results showed all matrices had higher accuracy scores than home matrices in all five models. The false negative rates were lower or equal for all matrices than home matrices for five machine learning models. The conclusion was drawn from the results that home-measured physical health indicators were less accurate than all physical indicators in predicting patients’ risk for heart disease. Therefore, without the future development of home-testable indicators, all physical health indicators are preferred in measuring the risk for heart diseases.

Machine Learning, Data Visualization, Feature Engineering, Health, Heart Disease

Sun, H. and Pan, J. (2023) Heart Disease Prediction Using Machine Learning Algorithms with Self-Measurable Physical Condition Indicators. Journal of Data Analysis and Information Processing, 11, 1-10. doi: 10.4236/jdaip.2023.111001.