Download PDFOpen PDF in browserSurvival Prediction of Heart Failure Patients Using Lasso Algorithm and Gaussian Naive Bayes ClassifierEasyChair Preprint 691221 pages•Date: October 22, 2021AbstractCardiovascular diseases kill approximately 17 million people globally per annum , and that they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when heart cannot pump enough blood to satisfy the requirements of the body. Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which may be wont to perform biostatistics analysis aimed toward highlighting patterns and correlations otherwise undetectable by medical doctors. Health plans must prioritize disease management efforts to scale back hospitalization and mortality rates in heart disease patients. We developed a risk model to predict the 5-year risk of mortality or hospitalization for heart disease among patients at an outsized health maintenance organization.While performing partitioning recursively, it sequences partitioning greedily instead of finding the optimal partitioning sequence. In proposed system, the LASSO algorithm is used to select features and classification using Gaussian Naïve Bayes, and investigate the results. Lasso and ridge regression with Gaussian Naïve Bayes (GNB) classifiers has given better results in most of the casess Keyphrases: Lasso algorithm, MachineLearning, Patient
|