Prof. Pasquale Arpaia, Dr. Renato Cuocolo, Prof. Roberto Prevete.
Goals
- A wearable soft sensor for cardiovascular risk assessment is conceptually designed;
- Non-invasive measures are exploited as well as the results of patients’ interview;
- Machine learning is adopted and Random Forest results as the best classifier for the assignment of a cardiovascular risk class;
- Online available data are employed for the design, notably for the classifier training.
Keywords
Artificial intelligence, cardiovascular risk assessment, stroke, soft sensors, decision support system.
Collaborations
A.O.U. Federico II.