Artificial Intelligence (AI) tools can prove to be helpful in keeping the heart healthy. This information has been revealed in a study conducted in London.
The study identified five sub-types responsible for heart attack with the help of AI. This study has been published in ‘The Lancet Digital Health Journal’.
The study explored five possible subtypes for heart attack, including early onset, late onset, association with atrial fibrillation, metabolic and cardiometabolic.
The study states that the use of machine learning can reduce heart attack cases to a great extent. For the study, the researchers analyzed data from people aged 30 or over who had ever had a heart attack and been diagnosed with a heart attack over the past 20 years in the UK. For the study, the researchers used four machine learning methods. methods used. This includes k-means, hierarchical, k-medoids and mixture model clustering.
Five Possible Subtypes for Heart Attack
- Early start
- Late start
- Related to Atrial Fibrillation
- Metabolism
- Cardiometabolic
Study of Apprehension of Death
The study also assessed the risk of death within one year in patients with all five subtypes of heart attack. It found 20 percent death within one year in patients with early symptoms, 46 percent death within one year in patients with late onset of heart attack symptoms, 61 percent death within one year in patients with atrial fibrillation. Percent of death, people with metabolism were found to be 37 percent more likely to die within a year.
Conclusion: Also Developed The App
Researchers have also developed an app that doctors can use to identify the type of heart attack. Along with this, their survival is also predicted.
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