Anomaly Detection in Heart Disease Using a Density-Based Unsupervised Approach

Anomaly Detection in Heart Disease Using a Density-Based Unsupervised Approach

Cardiovascular disease is one of the most common diseases in the modern world, which, if diagnosed early, can greatly reduce the damage to the patient. Diagnosis of heart disease requires great care, and in some cases, the process can be disrupted by human error. Machine learning methods, especially...

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Bibliographic Details
Main Authors: Aghbolagh, M.A (Author), Chen, J. (Author), Jamel, A.A.M (Author), Licai, Z. (Author), Nanehkaran, Y.A (Author), Navaei, Y.D (Author), Shengnan, Z. (Author)
Format: Article
Language:English
Published: Hindawi Limited 2022
Subjects:
Anomaly detection
Cardiology
Cardiovascular disease
Cluster analysis
Clustering methods
Common disease
Data mining
Density-based
Diagnosis
Diseases
Filtration
Forecasting
Heart
Heart disease
Heart patients
Human errors
Learning systems
Neighbourhood
Unsupervised approaches
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