TITLE:
Heart Disease Prediction Using Machine Learning Algorithms with Self-Measurable Physical Condition Indicators
AUTHORS:
Huating Sun, Jianan Pan
KEYWORDS:
Machine Learning, Data Visualization, Feature Engineering, Health, Heart Disease
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.11 No.1,
January
18,
2023
ABSTRACT: 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.