Prediction of the Survival of Patients with Cardiac Failure by using Soft Computing Techniques - Morani, Kinan; Eigner, Gyorgy; Ferenci, Tomas; Kovacs, Levente; Engin, Seref Naci (IEEE, 2018)
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: Artificial Neural Networks; Random Forest; Support Vector Machine; Decision Tree; Area Under Cover; Computer Science; Engineering