Abstract
The following paper presents a piece of work done on a relatively small dataset-with 1099 samples and 20 attributes-obtained from hospital records in Hungary. It goes to prove that by using a well tuned support vector machine model brought in better predicting results in terms of accuracy and calculation cost to a classification problem compared to an artificial neural network, random forest or the decision tree models. Next further improvements were suggested for the dataset and the preparation process as well.
Keywords
Decision tree
Artificial neural network
Random forest
Computer science
Support vector machine
Soft computing
Process (computing)
Artificial intelligence
Machine learning
Tree (set theory)
Work (physics)
Data mining
Engineering
Mathematics
Subject Areas
Artificial Intelligence in Healthcare
·Health Information Management ·Health Sciences
Cardiovascular Function and Risk Factors
·Cardiology and Cardiovascular Medicine ·Health Sciences
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