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Case Reports
. 2021 Jul 28:14:497-502.
doi: 10.2147/IMCRJ.S322827. eCollection 2021.

Machine Learning in an Elderly Man with Heart Failure

Affiliations
Case Reports

Machine Learning in an Elderly Man with Heart Failure

Joel Koops. Int Med Case Rep J. .

Abstract

Machine learning is a branch of artificial intelligence and can be used to predict important outcomes in a wide variety of medical conditions. With the widespread use of electronic medical records, the vast amount of data required for this process is now readily available. The following case demonstrates the application of machine learning to an elderly man with heart failure. The algorithms used, namely, decision tree and random forest, both correctly differentiated heart failure with preserved ejection fraction from heart failure with reduced ejection fraction. This has important treatment and prognostic ramifications and can be completed at the point of care while awaiting confirmation via echocardiogram. Viewing the machine learning process through a patient-centered lens, as in this case, highlights the key role we as physicians have in the implementation and supervision of machine learning.

Keywords: artificial intelligence; decision tree; heart failure; prediction; random forest.

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Conflict of interest statement

The author reports no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Decision tree for an 85-year-old male with heart failure. Value corresponds to the number of samples in each node that belong to HFpEF and HFrEF, respectively. Gini is a measure of the impurity at each node and parallels the disparity of the values at each location. Diabetes is a Boolean value where 0 is false and 1 is true.
Figure 2
Figure 2
Single arbitrarily chosen decision tree from the random forest approach for an 85-year-old male with heart failure. Value corresponds to the number of samples in each node that belong to HFpEF and HFrEF, respectively. Gini is a measure of the impurity at each node and parallels the disparity of the values at each location. Anemia and hypertension are Boolean values where 0 is false and 1 is true.
Figure 3
Figure 3
Comparison of feature importance between decision tree and random forest ML in an 85-year-old male with heart failure. This has been normalized so that the total attribute contribution for each corresponding method adds up to 1.

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