Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set
نویسندگان
چکیده
Learning systems have been focused on creating models capable of obtaining the best results in error metrics. Recently, focus has shifted to improvement interpretation and explanation results. The need for is greater when these are used support decision making. In some areas, this becomes an indispensable requirement, such as medicine. goal study was define a simple process construct system that could be easily interpreted based two principles: (1) reduction attributes without degrading performance prediction (2) selecting technique interpret final system. To describe process, we selected problem, predicting cardiovascular disease, by analyzing well-known Statlog (Heart) data set from University California’s Automated Repository. We analyzed cost making predictions easier reducing number features explain classification health status versus accuracy. performed analysis large techniques metrics, demonstrating it possible explainable reliable provide high quality predictive performance.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2021
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app11031285