Abstract: Heart attack is one of the most alarming problem faced by individuals in today’s world. Lakhs of people die each year due to heart attack. This can be attributed to growing work pressure, mental stress, unhealthy food habits and ignorance towards ones health. For this one is often advised to consult a doctor regarding possible risks his/her heart may have and the appropriate measures to be taken to mitigate the risk. For this the patient’s report has to be carefully scrutinized by the doctor to find out any possible risk. However, this careful study of the patient’s report is time consuming and requires a lot of effort. Our project aims to reduce this effort and time required by developing a model to predict the risk an individual may have of suffering from a heart attack. An implementation of such a model is discussed in the upcoming sections. Also this model is accompanied by a user interface making it easy to use for the end user. The implementation of this model uses the dataset which includes data collected from various patients about all the relevant parameters required for the prediction of heart attack risk. The data is classified using the right algorithm to predict the level of risk the individual has of suffering from a heart attack.
Keywords: Heart attack, Risk, Prediction, Machine learning, Neural network.
| DOI: 10.17148/IJARCCE.2020.9404