Early Detection of Heart Attack Using Machine Learning

Cardiovascular diseases are a group of conditions that harm the heart and blood vessels. These illnesses may have an impact on one or more regions of the heart and blood vessels. A person may or may not have symptoms. The leading cause of mortality worldwide over the past several decades has been cardiovascular disease (CVD), which has become the most serious illness worldwide, not only in India. It has been demonstrated that machine learning is useful with the decision-making process and in making predictions from the vast data gathered by the healthcare industry. So, in this paper, a faster method is used to predict heart attack efficiently. Random forest algorithm is used in this method; the attributes are selected at random and trained. Using the trained data, the test data will be compared along with the trained data to predict the result. As the attributes are selected at random to train the data, the performance will increase. A voting is calculated for each decision tree in the random forest algorithm to find out the most significant result.