Machine learning-based cardiovascular risk assessment

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.