Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The identification of optimal candidates for ventricular assist device (VAD) therapy is of great importance for future widespread application of this life-saving technology. During recent years, numerous traditional statistical models have been developed for this task. In this study,  compared three different supervised machine learning techniques for risk prognosis of patients on VAD: Decision Tree, Support Vector Machine (SVM) and Bayesian Tree-Augmented Network, to facilitate the candidate identification. A predictive (C4.5) decision tree model was ultimately developed based on 6 features identified by SVM with assistance of recursive feature elimination. This model performed better compared to the popular risk score of Lietz et al. with respect to identification of high-risk patients and earlier survival differentiation beten high- and low-risk candidates.
['Yajuan Wang', 'Carolyn Penstein Rosé', 'Antonio Ferreira', 'Dennis M. McNamara', 'Robert L. Kormos', 'James F. Antaki']
I am writing a research paper on a idea A Classification Approach for Risk Prognosis of Patients on Mechanical Ventricular Assistance
[{"from": "human", "value": "I am writing a research paper on a idea A Classification Approach for Risk Prognosis of Patients on Mechanical Ventricular Assistance"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The identification of optimal candidates for ventricular assist device (VAD) therapy is of great importance for future widespread application of this life-saving technology. During recent years, numerous traditional statistical models have been developed for this task. In this study,  compared three different supervised machine learning techniques for risk prognosis of patients on VAD: Decision Tree, Support Vector Machine (SVM) and Bayesian Tree-Augmented Network, to facilitate the candidate identification. A predictive (C4.5) decision tree model was ultimately developed based on 6 features identified by SVM with assistance of recursive feature elimination. This model performed better compared to the popular risk score of Lietz et al. with respect to identification of high-risk patients and earlier survival differentiation beten high- and low-risk candidates."}]