%0 Journal Article %1 noauthororeditor %A |, R. Regin | S. Suman Rajest | Shynu T | Steffi. R %D 2023 %J International Journal of Human Computing Studies %K Algorithm Attack, Disease, Electrocardiogram, Heart Learning, Machine Networks Neural Prediction of %N 4 %P 5-25 %T The Application of Machine Learning to the Prediction of Heart Attack %U https://journals.researchparks.org/index.php/IJHCS/article/view/4259/3999 %V 5 %X Heart illnesses are among the most significant contributors to mortality in the world in the modern era. Heart attacks are responsible for the death of one person every 33 seconds. disease of the cardiovascular system by disclosing the proportion of mortality all over the world that are caused by heart attacks. In order to forecast instances of heart disease, a supervised machine learning method is utilised. Because the incidence of heart strokes in younger people is growing at an alarming rate, we need to establish a method that can identify the warning signs of a heart attack at an early stage and stop the stroke before it occurs. Because it is impractical for the average person to often undertake expensive tests like the electrocardiogram (ECG), there is a need for a system that is convenient and, at the same time, accurate in forecasting the likelihood of developing heart disease. Therefore, our plan is to create a programme that, given basic symptoms such as age, sex, pulse rate, etc., can determine whether or not a person is at risk for developing a cardiac condition. The machine learning algorithm neural networks that are used in the suggested system are the most accurate and dependable.

@article{noauthororeditor, abstract = {Heart illnesses are among the most significant contributors to mortality in the world in the modern era. Heart attacks are responsible for the death of one person every 33 seconds. disease of the cardiovascular system by disclosing the proportion of mortality all over the world that are caused by heart attacks. In order to forecast instances of heart disease, a supervised machine learning method is utilised. Because the incidence of heart strokes in younger people is growing at an alarming rate, we need to establish a method that can identify the warning signs of a heart attack at an early stage and stop the stroke before it occurs. Because it is impractical for the average person to often undertake expensive tests like the electrocardiogram (ECG), there is a need for a system that is convenient and, at the same time, accurate in forecasting the likelihood of developing heart disease. Therefore, our plan is to create a programme that, given basic symptoms such as age, sex, pulse rate, etc., can determine whether or not a person is at risk for developing a cardiac condition. The machine learning algorithm neural networks that are used in the suggested system are the most accurate and dependable.}, added-at = {2023-07-06T13:20:30.000+0200}, author = {|, R. Regin | S. Suman Rajest | Shynu T | Steffi. R}, biburl = {https://www.bibsonomy.org/bibtex/2e1c3dd46807d32ee1a9be964761df1df/researchpark_20}, interhash = {9f112838ea056a4eb559b344a1f77da0}, intrahash = {e1c3dd46807d32ee1a9be964761df1df}, issn = {2615-8159}, journal = {International Journal of Human Computing Studies}, keywords = {Algorithm Attack, Disease, Electrocardiogram, Heart Learning, Machine Networks Neural Prediction of}, language = {english}, month = apr, number = 4, pages = {5-25}, timestamp = {2023-07-06T13:20:30.000+0200}, title = {The Application of Machine Learning to the Prediction of Heart Attack}, url = {https://journals.researchparks.org/index.php/IJHCS/article/view/4259/3999}, volume = 5, year = 2023 }