Towards Predicting Risk of Coronary Artery Disease from Semi-Structured Dataset

Many kinds of disease-related data are now available and researchers are constantly attempting to mine useful information out of these. Medical data are not always homogeneous and in structured form, and mostly they are time-stamped data. Thus, special care is required to prevent any kind of informa...

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Bibliographic Details
Published in:Interdisciplinary sciences : computational life sciences 2020-12, Vol.12 (4), p.537-546
Main Authors: Roy, Smita, Ekbal, Asif, Mondal, Samrat, Desarkar, Maunendra Sankar, Chattopadhyay, Shubham
Format: Article
Language:English
Subjects:
Algorithms
Biomedical and Life Sciences
Cardiovascular disease
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Appl. in Life Sciences
Coronary artery
Coronary artery disease
Coronary vessels
Data mining
Datasets
Health Sciences
Heart diseases
Learning algorithms
Life Sciences
Machine learning
Mathematical and Computational Physics
Medical research
Medicine
Original Research Article
Prediction models
Risk
Statistics for Life Sciences
Theoretical
Theoretical and Computational Chemistry
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Summary:Many kinds of disease-related data are now available and researchers are constantly attempting to mine useful information out of these. Medical data are not always homogeneous and in structured form, and mostly they are time-stamped data. Thus, special care is required to prevent any kind of information loss during mining such data. Mining medical data is challenging as predicting the non-accurate result is often not acceptable in this domain. In this paper, we have analyzed a partially annotated coronary artery disease (CAD) dataset which was originally in a semi-structured form. We have created a set of some well-defined features from the dataset, and then build predictive models for CAD risk identification using different supervised learning algorithms. We then further enhanced the performances of the models using a feature selection technique. Experiments show that results are quite interesting, and are expected to help medical practitioners for investigating CAD risk in patients.
ISSN:1913-2751
1867-1462
DOI:10.1007/s12539-020-00363-x