Prediction of Sudden Cardiac Death Using Ensemble Classifiers

Sudden Cardiac Death (SCD) is a medical problem that is responsible for over 300,000 deaths per year in the United States and millions worldwide. SCD is defined as death occurring from within one hour of the onset of acute symptoms, an unwitnessed death in the absence of pre-existing progressive circulatory failures or other causes of deaths, or death during attempted resuscitation. Sudden death due to cardiac reasons is a leading cause of death among Congestive Heart Failure (CHF) patients. The use of Electronic Medical Records (EMR) systems has made a wealth of medical data available for research and analysis. Supervised machine learning methods have been successfully used for medical diagnosis. Ensemble classifiers are known to achieve better prediction accuracy than its constituent base classifiers. In an effort to understand the factors contributing to SCD, data on 2,521 patients were collected for the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT). The data included 96 features that were gathered over a period of 5 years. The goal of this dissertation was to develop a model that could accurately predict SCD based on available features. The prediction model used the Cox proportional hazards model as a score and then used the ExtraTreesClassifier algorithm as a boosting mechanism to create the ensemble. We tested the system at prediction points of 180 days and 365 days. Our best results were at 180-days with accuracy of 0.9624, specificity of 0.9915, and F1 score of 0.9607

Classification of cardiac arrhythmia by random forests with features constructed by kaizen programming with linear genetic programming

Cardiac rhythm disorders may cause severe heart diseases, stroke, and even sudden cardiac death. Some arrhythmias are so serious that can cause injury to other organs, for instance, brain, kidneys, lungs or liver. Therefore, early and correct diagnosis of cardiac arrhythmia is essential to the prevention of serious problems. There are expert systems to classify arrhythmias from electrocardiograms signals. However, it has been shown that not only selecting the correct features from the dataset but also generating combined features could be the key to having real progress in classification. Therefore, this paper investigates a novel hybrid evolutionary technique to perform both tasks at the same time, finding complementary features that cover different characteristics of the data. The new features were tested with a widely-used classifier called Random Forests. The method reduced a dataset with 279 attributes to 26 attributes and achieved accuracies of 86.39% for binary classification and 77.69% for multiclass. Our approach outperformed several popular feature selection, feature generation, and state-of-the-art related work from the literature.Institute of Science and Technology (ICT) Federal University of São Paulo (UNIFESP) São José dos Campos, SP, BrazilInstitute of Science and Technology (ICT) Federal University of São Paulo (UNIFESP) São José dos Campos, SP, BrazilWeb of Scienc

Iowa State University, Courses and Programs Catalog 2014–2015

The Iowa State University Catalog is a one-year publication which lists all academic policies, and procedures. The catalog also includes the following: information for fees; curriculum requirements; first-year courses of study for over 100 undergraduate majors; course descriptions for nearly 5000 undergraduate and graduate courses; and a listing of faculty members at Iowa State University.https://lib.dr.iastate.edu/catalog/1025/thumbnail.jp