Using Machine Learning to Predict the Likelihood of Heart Attacks

Abstract

Cases of heart disease is increasing at an alarming rate, and it is critical and concerning to predict any such diseases in advance. This is a difficult task that must be completed precisely and efficiently. Decisions are made based on the doctor's prior experience dealing with similar issues. This leads to errors and high costs, which have an impact on the quality of medical services. Utilizing analytic tools and data modelling can aid in improving clinical judgements. The data science lifecycle is intended for big data concerns and data science projects. In general, a data science project consists of seven steps: problem description, data collection, data preparation, data exploration, data modelling, model evaluation, and model deployment. This project follows the data science lifecycle to create a web application for heart attack prediction.