Using Logistic Regression to Build a Model for Predicting Classification of Cardiac Catheterization
Journal Title: Libyan Journal of Engineering Science and Technology - Year 2021, Vol 1, Issue 2
Abstract
Machine learning is considered a tool for solving several problems in many disciplines such as health domain (cardiovascular diseases). Cardiac catheterization can be exploited for diagnosis and treatment of cardiovascular diseases. Catheterization has hazardous effects and consequences to individual’s life. Therefore, the research aims to build a classifier, which can be used to predict whether a patient needs to be followed up by the doctor after a cardiac catheterization procedure, or not. Logistic regression is the supervised learning algorithm, which is interested in constructing a model. Classifier can be utilized in for predicting in unseen cases. The principal component analysis is utilized to minimize the number of features. It was discovered that the Logistic regression algorithm has great results when the number of the features smaller comparing to larger number of the features
Authors and Affiliations
Tahani M. A. Kasih¹, Eimad R. Gadalla²
Keywords
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