The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Principles of data mining
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data mining for indicators of early mortality in a database of clinical records
Artificial Intelligence in Medicine
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
Data Mining in Healthcare and Biomedicine: A Survey of the Literature
Journal of Medical Systems
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In this paper, we describe a novel methodology which employs machine learning as an alternative means to explore hospital characteristics and client satisfaction, for decision making and improved quality of care. We applied well known feature selection and data mining algorithms such as forward selection and Naïve Bayes respectively, to determine patient satisfaction, which is an important indicator of quality of care in hospital settings. Our dataset comprised of three types of data, (i) patient perception about received care, (ii) nurse perception about the working environment and (iii) organizational attributes of the hospital. Our experimental results exhibited high classification accuracy (87%), allowing valid conclusions to be reached about the organizational and workforce factors which attribute to patient satisfaction. Our findings were validated using traditional statistical methods such as binomial correlation and linear regression.