This paper describes an artificial-intelligence–assisted screening system implemented to support medical cardiovascular examinations performed by doctors. The proposed system is a two-stage supervised classifier comprising a convolutional neural network for heart murmur detection and a decision tree for classifying vital signs. The classifiers are trained to prioritize higher-risk individuals for more time-efficient assessment. A feature selection approach is applied to maximize classification accuracy by using only the most significant vital signs correlated with heart issues. The results suggest that the trained convolutional neural network can learn and detect heart sound anomalies from the time-domain and frequency-domain signals without using any user-guided mathematical or statistical features. It is also concluded that the proposed two-stage approach improves diagnostic reliability and efficiency.

Two-stage artificial intelligence clinical decision support system for cardiovascular assessment using convolutional neural networks and decision trees

Lundgren J.;Carratu M.;Liguori C.
2020-01-01

Abstract

This paper describes an artificial-intelligence–assisted screening system implemented to support medical cardiovascular examinations performed by doctors. The proposed system is a two-stage supervised classifier comprising a convolutional neural network for heart murmur detection and a decision tree for classifying vital signs. The classifiers are trained to prioritize higher-risk individuals for more time-efficient assessment. A feature selection approach is applied to maximize classification accuracy by using only the most significant vital signs correlated with heart issues. The results suggest that the trained convolutional neural network can learn and detect heart sound anomalies from the time-domain and frequency-domain signals without using any user-guided mathematical or statistical features. It is also concluded that the proposed two-stage approach improves diagnostic reliability and efficiency.
2020
978-989758398-8
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4746583
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact