학술논문

Monitoring and Predicting of Heart Diseases Using Machine Learning Techniques
Document Type
Conference
Source
2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Convergence in Technology (I2CT), 2023 IEEE 8th International Conference for. :1-4 Apr, 2023
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Heart
Machine learning algorithms
Neural networks
Machine learning
Medical services
Predictive models
Prediction algorithms
Heart disease
IoT
Monitoring and prediction
Convolutional neural networks
Language
Abstract
Deaths due to heart disease are on the rise. Old people as well as relatively young people are succumbing to death because of heart failures. Chronic heart failures affect more than 26 millions people worldwide. This number is increasing 2% annually. Therefore it is needed for accurate diagnosis and at accurate time. The Healthcare industry produces lots of data. But this data is unstructured and not accessible easily. This data is not studied properly to extract information out of data. By using modern techniques like data mining and machine learning techniques we can get knowledge that can be used to diagnose heart diseases. Today a lot of research is happening on prediction of heart diseases. It is really important to study this research comprehensively. The sole target of this review paper is to study and summarize recent research carried out on heart disease predictions.