Enhancing cardiac disease detection via a fusion of machine learning and medical imaging.

Authors

Yu T,Chen K

Affiliations (2)

  • School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, ZheJiang, China.
  • Fifth School of Clinical Medicine of Zhejiang Chinese Medical University , Huzhou Cental Hospital, Huzhou, ZheJiang, China. [email protected].

Abstract

Cardiovascular illnesses continue to be a predominant cause of mortality globally, underscoring the necessity for prompt and precise diagnosis to mitigate consequences and healthcare expenditures. This work presents a complete hybrid methodology that integrates machine learning techniques with medical image analysis to improve the identification of cardiovascular diseases. This research integrates many imaging modalities such as echocardiography, cardiac MRI, and chest radiographs with patient health records, enhancing diagnosis accuracy beyond standard techniques that depend exclusively on numerical clinical data. During the preprocessing phase, essential visual elements are collected from medical pictures utilizing image processing methods and convolutional neural networks (CNNs). These are subsequently integrated with clinical characteristics and input into various machine learning classifiers, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, and Deep Neural Networks (DNNs), to differentiate between healthy persons and patients with cardiovascular illnesses. The proposed method attained a remarkable diagnostic accuracy of up to 96%, exceeding models reliant exclusively on clinical data. This study highlights the capability of integrating artificial intelligence with medical imaging to create a highly accurate and non-invasive diagnostic instrument for cardiovascular disease.

Topics

Machine LearningImage Processing, Computer-AssistedHeart DiseasesCardiovascular DiseasesDiagnostic ImagingJournal Article

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.