Key result
Machine learning algorithms using only self-measurable physical condition indicators achieved a maximum accuracy of 73.77%, compared to 88.52% when using all physical condition indicators.
Why the study?
Heart disease cases and associated mortality are increasing, and advanced equipment allows home health measurement; this study aimed to determine the accuracy of self-measurable physical health indicators compared to all provider-measured indicators for predicting heart disease using machine learning models.
Does using only self-measurable physical condition indicators reduce the accuracy of machine learning models in predicting heart disease compared to using all clinical indicators?
Does using only self-measurable physical condition indicators reduce the accuracy of machine learning models in predicting heart disease compared to using all clinical indicators?
Machine learning models using only home-measurable physical indicators are currently less accurate and have higher false negative rates for predicting heart disease compared to models using a full clinical panel.
Self-measurable indicators limit ML heart disease prediction accuracy; leaves open whether refined home-based models can close the gap.
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.
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Sun et al. (2023) studied Heart disease (n=303). Self-measurable physical condition indicators (home matrices) vs. All physical condition indicators (all matrices) was evaluated on Accuracy score for predicting heart disease (Logistic Regression). Machine learning algorithms using only self-measurable physical condition indicators achieved a maximum accuracy of 73.77%, compared to 88.52% when using all physical condition indicators.
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