Cardiovascular Disease Detection and Future Implementations of Machine Learning
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Tampa
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Manh-Huong Phan
Description
Cardiovascular diseases (CVD) are the leading cause of death worldwide. It is predicted that CVD will remain the leading cause of death and will account for more than 23 million deaths in 2030 globally. Early detection of these diseases, as well as early intervention, can give the patient their best chance of overcoming the disease. There are new methods of detection that are much less invasive compared to methods such as, for example, coronary catheterization. The innovative methods include phone apps, smartwatches, and patches that the user can easily add to their life. It is important to track cardiac health, and these methods make it very easy and practical. These devices collect data that can be used to model CVD using machine learning algorithms. This can ultimately lead to an earlier diagnosis which drastically increases the chances of survival. The objective is to analyze how different devices monitor cardiac health and how machine learning can be implemented. It was found that these new devices are effective in non-intrusively collecting cardiac health data, which allows the creation of predictive models using machine learning. Detection of CVD remains instrumental in order to decrease mortality associated with these diseases. CVD detection with the implementation of machine learning is a pressing topic with a very promising future.
Cardiovascular Disease Detection and Future Implementations of Machine Learning
Cardiovascular diseases (CVD) are the leading cause of death worldwide. It is predicted that CVD will remain the leading cause of death and will account for more than 23 million deaths in 2030 globally. Early detection of these diseases, as well as early intervention, can give the patient their best chance of overcoming the disease. There are new methods of detection that are much less invasive compared to methods such as, for example, coronary catheterization. The innovative methods include phone apps, smartwatches, and patches that the user can easily add to their life. It is important to track cardiac health, and these methods make it very easy and practical. These devices collect data that can be used to model CVD using machine learning algorithms. This can ultimately lead to an earlier diagnosis which drastically increases the chances of survival. The objective is to analyze how different devices monitor cardiac health and how machine learning can be implemented. It was found that these new devices are effective in non-intrusively collecting cardiac health data, which allows the creation of predictive models using machine learning. Detection of CVD remains instrumental in order to decrease mortality associated with these diseases. CVD detection with the implementation of machine learning is a pressing topic with a very promising future.
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