Analysis of an Intelligent Pulse Monitoring Beat Wiser System using Machine Learning Classifiers

In recent decades, vascular disease—which includes a variety of heart-related conditions—has emerged as the primary cause of death worldwide. There are numerous risks associated with heart disease, and it is important to have accurate, trustworthy, and sensible techniques for early detection in order to promptly manage the condition. One popular technique for analyzing large volumes of data in the healthcare industry is machine learning. Machine learning is a widely employed method for analyzing vast amounts of data in the field of healthcare. This paper focused on hyper parameter tuning of multi-classifiers to forecast the occurrence of various heart illnesses in patients and reduce the problem of over fitting. The dataset comprises 14 primary attributes with a proposed framework driven by multi classifier to get predictions. The results indicate that the LGBM classifier achieves the highest accuracy score with 99.7%.