Heart Failure Prediction using Supervised ML/AI Technique
Introduction This project is aimed to support ESC guidelines [1] that help health professionals manage people with heart failure (HF) according to the best available evidence. The objective is not only to develop an accurate survival prediction model but also to discover essential factors for the survival prediction of HF patients. The complex nature of HF produces a significant amount of information that is too difficult for clinicians to process as it requires simultaneous consideration of multiple factors and their in teractions [2,3]. ML/AI techniques can be utilized in this scenario to develop a reliable decision support system to assist clinicians in properly interpreting the patients ’ records to make informed decisions [2-5]. Workflow Let us install Anaconda IDE, upgrade pip and create a virtual environment Jupyter. The Python-3 ML/AI workflow consists of the following steps: Step 1: Inst...