NDLI: Issues in the Mining of Heart Failure Datasets

Content Provider	Springer Nature Link
Author	Poolsawad, ngnuch Moore, Lisa Kambhampati, Chandrasekhar Cleland, John G. F.
Copyright Year	2014
Abstract	This paper investigates the characteristics of a clinical dataset using a combination of feature selection and classification methods to handle missing values and understand the underlying statistical characteristics of a typical clinical dataset. Typically, when a large clinical dataset is presented, it consists of challenges such as missing values, high dimensionality, and unbalanced classes. These pose an inherent problem when implementing feature selection and classification algorithms. With most clinical datasets, an initial exploration of the dataset is carried out, and those attributes with more than a certain percentage of missing values are eliminated from the dataset. Later, with the help of missing value imputation, feature selection and classification algorithms, prognostic and diagnostic models are developed. This paper has two main conclusions: 1) Despite the nature of clinical datasets, and their large size, methods for missing value imputation do not affect the final performance. What is crucial is that the dataset is an accurate representation of the clinical problem and those methods of imputing missing values are not critical for developing classifiers and prognostic/diagnostic models. 2) Supervised learning has proven to be more suitable for mining clinical data than unsupervised methods. It is also shown that non-parametric classifiers such as decision trees give better results when compared to parametric classifiers such as radial basis function networks (RBFNs).
Starting Page	162
Ending Page	179
Page Count	18
File Format	PDF
ISSN	14768186
Journal	International Journal of Automation and Computing
Volume Number	11
Issue Number	2
e-ISSN	17518520
Language	English
Publisher	Springer-Verlag
Publisher Date	2015-03-12
Publisher Place	Berlin, Heidelberg
Access Restriction	Subscribed
Subject Keyword	Heart failure clinical dataset classification clustering missing values feature selection Control, Robotics, Mechatronics Computer Applications Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Control and Systems Engineering Modeling and Simulation Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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