Are Random Forests Better than Support Vector Machines for . . . Our experiments using 18 diagnostic and prognostic datasets show that support vector machines outperform random forests often by a large margin Gene expression microarrays are becoming increasingly promising for clinical decision support in the form of diagnosis and prediction of clinical outcomes of cancer and other complex diseases
Robust Predictive Models in Clinical Data—Random Forest and . . . In this chapter, we aim to explain the principles that make random forest (RF) and support vector machines (SVMs) successful modelling and prediction tools for a variety of applications We try to achieve this by presenting the basic ideas of RF and SVMs, together
A Random Forest based predictor for medical data . . . We present highly accurate predictors for 10 different diseases, as well as suggest a methodology that is sufficiently general and is expected to perform well for other diseases with similar datasets
Use of survival support vector machine combined with random . . . Using the SEER (The Surveillance, Epidemiology, and End Results) database data on NPC patients, we proposed the use of random survival forest (RSF) and survival-support vector machine (SVM) from the machine learning methods to develop a survival prediction system specifically for NPC patients
An Overview on the Advancements of Support Vector Machine . . . Support vector machines (SVMs) are well-known machine learning algorithms for classification and regression applications In the healthcare domain, they have been used for a variety of tasks including diagnosis, prognosis, and prediction of disease outcomes
Utilizing Random Forests for High-Accuracy Classification in . . . By analyzing large, heterogeneous medical datasets, we demonstrate how Random Forests can outperform traditional classification algorithms, offering high precision in identifying and classifying diseases