Implementation of Support Vector Machine and Random Forest for Heart Failure Disease Classification
DOI:
https://doi.org/10.57152/predatecs.v1i1.816Keywords:
Classification, Comparison, Heart Failure, Random Forest, Support Vector MachineAbstract
Heart failure is a life-threatening disease and its management should be considered a global public health priority. The use of data mining in data processing operations to identify existing patterns and identify the information stored in them. In this study, researchers classify using two algorithms for comparison of algorithms, namely Random Forest (RF) and Support Vector Machine (SVM). The purpose of this study is to find patterns in finding the best accuracy for the 2 algorithms. The results of this study obtained an accuracy of 81.51%. with a Hold Out of 60 : 40% on the SVM algorithm, while an accuracy of 83.33 % with a Hold Out of 9 0 : 1 0% on the R F algorithm . From these results it can be seen that the highest accuracy value is obtained at RF making the best algorithm compared to the SVM algorithm.
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