Porto, R.; Molina, J.M.M.; Berlanga, A.; Patricio, M.A.A. Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set. Appl. Sci.2021, 11, 1285.
Porto, R.; Molina, J.M.M.; Berlanga, A.; Patricio, M.A.A. Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set. Appl. Sci. 2021, 11, 1285.
Porto, R.; Molina, J.M.M.; Berlanga, A.; Patricio, M.A.A. Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set. Appl. Sci.2021, 11, 1285.
Porto, R.; Molina, J.M.M.; Berlanga, A.; Patricio, M.A.A. Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set. Appl. Sci. 2021, 11, 1285.
Abstract
Learning systems have been very focused on creating models that are capable of obtaining the best results in error metrics. Recently, the focus has shifted to improvement in order to interpret and explain their results. The need for interpretation is greater when these models are used to support decision making. In some areas this becomes an indispensable requirement, such as in medicine. This paper focuses on the prediction of cardiovascular disease by analyzing the well-known Statlog (Heart) Data Set from the UCI’s Automated Learning Repository. This study will analyze the cost of making predictions easier to interpret by reducing the number of features that explain the classification of health status versus the cost in accuracy. It will be analyzed on a large set of classification techniques and performance metrics. Demonstrating that it is possible to make explainable and reliable models that have a good commitment to predictive performance.
Keywords
Interpretable Artificial Intelligence; Cardiovascular disease prediction; Machine Learning in Healthcare
Subject
MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
