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Heart Failure Prediction using Supervised ML/AI Technique

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  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: Install/import key libraries import numpy as np # linear algebra import pandas