Predicting Cardiac Arrest Using Machine Learning with Zero Coding

Abstract

Heart disease cases are rising rapidly day by day, thus it's very difficult to predict any likely illness in advance. But to predict a heart ailment in advance is not an easy task. The consequences of several medical and pathological tests have to be combined in a complicated manner in order to diagnose cardiac disease. Doctors, researchers, and academicians are extremely interested in how Artificial Intelligence and Machine Learning can be used to anticipate heart illnesses because of the ominous complexity of the problem and the outstanding improvements in machine learning technology. ML has previously generated forecasts that were fast, precise, and efficient. However, the main obstacle to using machine learning in this field appears to be a shortage of coding skills. And here comes the solution: Alteryx Predictive Modeling. The cardiac disease patient dataset will be carefully and interactively analyzed as part of this research project, It also utilizes Alteryx for automated data processing. The dataset contains the parameters which are diagnosed as major medical conditions contributing to cardiac arrest. Then, the machine model is trained and predictions are made with XGBoost Classifier. We've utilized Predictive Modeling with Alteryx, which works kind of like a semi-automated machine learning process because it allows choosing certain parameters manually and handles many machine learning tasks automatically, it really speeds up creating models. Plus, you don't need to write any code at all.