Machine Learning Can Predict Heart Attack or Death More Accurately Than Us.......

 Hey! Did you know that Artificial Intelligence can be used to 'predict Deadly heart attacks up to 5 years in advance'

In new research, our machine learning professionals have found that Artificial intelligence could be used to predict those at risk of a deadly heart attack up to 5 years in advance. 

A branch of artificial intelligence, machine learning has become more accurate than our medical professionals in predicting the incidence of heart attack or death in patients at risk of coronary artery disease.

Heart Attack

The University of Oxford's experts has developed a "fingerprint", or "biomarker", by using machine learning.

When a patient with chest pain is admitted to the hospital, it's standard procedure for a coronary CT angiogram (CCTA) to be performed. If there is no narrowing in arteries detected-about 75% of cases-then the patient is sent home-yet some of them suffer a heart attack in the future.

  Los Angeles: In research of the University of Oxford researchers have stated that Artificial intelligence can be better at predicting heart attacks and cardiac deaths than the standard clinical tests, according to study. The result of the research was presented at the European Society of Cardiology Congress whereas it was published in the European Heart Journal.

To assess the risk of myocardial infarction or heart attack and cardiac death, researchers from Cedars-Sinai used machine learning. Then they compared with the actual experiences of the subjects over fifteen years.

 

Artificial intelligence

An application of Artificial intelligence "machine learning"  gives the computer the ability to automatically learn and improve from experience. By using machine learning they have developed the FRP fingerprint which captures the level of risk. The more heart scans which are added to the system then the prediction will become more accurate and the more information that will become core knowledge.

Let’s understand the term “Machine Learning”

 

Machine learning is a technique of data analysis by which we automate analytical model building. Machine learning is considered as a branch of Artificial Intelligence based on the idea that systems can learn from data, with minimal human intervention we can identify patterns and make decisions.

A complex algorithm or source code is built into a computer. Machine learning can be used in different fields like in investing, advertising, lending, organizing news, fraud detection, and more.

To create a good machine learning system, the following is the requirement

·             Data preparation capabilities

·         Algorithms-basic and advanced

·         Automation and iterative processes

·         Scalability

·         Ensemble modeling

 

Machine learning is used for various reasons in different sectors.  A model is created by the programming code that identifies the data and builds predictions around the data it identifies. In a lending institution, machine learning can be used to incorporate to predict bad loans and build a credit risk model. Machine learning is used by incorporation hubs to cover huge amounts of news stories from all corners of the world. Marketing and e-commerce platform also use machine learning for better and accurate transactions.

 Fraud detection tools can be created by machine learning in banks. Investment players in the securities market such as financial researchers, analysts, asset managers, and individual investors scour through a lot of information from different companies around the world to make profitable investment decisions. Machine learning is a method by which we study about computer algorithms that improve automatically through experience. Sometimes it is termed as a subset of Artificial Intelligence. In machine learning, it is difficult or infeasible to develop conventional algorithms to perform needed tasks.

Computational statics is closely related to machine learning, which focuses on making predictions using computers. In the field of machine learning, the study of mathematical optimization delivers methods, theory, and application domains. Machine learning involves computers discovering.

 

Approaches to Machine learning

 

There are three broad categories in which machine learning is divided traditionally, depending on the nature of the “signal” or “feedback” available to the learning system:

·         Supervised learning: Whenever we present a computer, it is presented with example inputs and their desired outputs, given by a teacher, and the goal is to learn a general rule that maps inputs to outputs.

·         Unsupervised learning: There is no label given to the learning algorithm, leaving it on its own to find structure in its input. We can say that unsupervised learning can be a goal in itself.

·         Reinforcement learning: It is a type of computer program that interacts with a dynamic environment in which it must perform a certain goal like driving a vehicle or playings game against an opponent.

 

  There is branch of theoretical computer science known as computational learning theory, which deals with the computational analysis of machine learning algorithms and their performances. Learning theory usually does not yield guarantees of the performance of algorithms because training sets are finite and the future is uncertain. Instead, probabilistic bounds which are present in performances are quite common.

 

Machine learning employs approaches to discipline in a very good way. Machine learning is something that focuses on the development of computer programs that can access data and use it to learn for themselves. As of 2020, In the field of machine learning deep learning has become the dominant approach for much ongoing work in the field of machine learning.

 

These are the following applications of machine learning:

 

Ø  Agriculture

Ø  Anatomy

Ø  Affective computing

Ø  Banking

Ø  Bioinformatics

Ø  Brain-machine interfaces

Ø  Citizen science

Ø  Computer vision

Ø  Economics

Ø  Data quality

Ø  Financial market

Ø  General game playing

Ø   Handwriting recognition

Ø  Robot locomotion

 

And many more like this……

 Limitations of machine learning

 In some field machine-learning has been transformative but in sometimes machine-learning programs failed to deliver expected results. And the reason for this is numerous such as lack of data, lack of access to the data, data bias, privacy the problem, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.

Let’s take example of an accident from 2018, in that accident a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.