Prediction Of Cardiovascular Diseases Using Neural Networks And Machine Learning
R Jegedeesan, T. Karpagam, K Jayashree · 2022
The field of data analysis, which is experiencing rapid growth, plays an important role in the field of health care. The health industry has become an enormous enterprise. The healthcare industry generates massive volumes of data on a day-to-day basis. This data makes it possible to extract hidden information, which is helpful for anticipating the infection as early as possible. In the medical field, the anticipation of cardiac disease is treated as one of the multifaceted tasks. With a surge in stroke rates in the younger age groups, we must set up a system to detect the symptoms of a stroke at an early stage and therefore prevent it. It is inconvenient for an ordinary man to be frequently subjected to expensive tests such as ECG. As a result, we must put in place a practical and reliable system to predict the risks of heart disease. Hence, we propose developing an app that can predict the vulnerability of a heart disease based on basic symptoms such as age, sex, pulse, cholesterol, and so on. The machine learning algorithm Random Forest turned out to be the most precise and reliable algorithm and, as a consequence, is used in the proposed system. The impact of AI is vital in anticipating the infection. Machine learning plays a crucial part in predicting cardiac arrest. In this project, several machine learning methods were used and their individual performances were compared to predict heart attack. The different techniques are, namely, decision trees, random forest, XGBoost, and neural networks.