A Cluster-Based Machine Learning Model for Large Healthcare Data Analysis

• Published in: Big Data Innovations and Applications pp. 92 - 106
• Main Authors: Sharifi, Fatemeh, Mohammed, Emad, Crump, Trafford, Far, Behrouz H.
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2019
• Series: Communications in Computer and Information Science
• Subjects: Big data; Dimension reduction; Machine learning; Patient quality of life
• ISBN: 9783030273545; 3030273547
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-030-27355-2_7

There is huge growth in the amount of patient survey data being generated in healthcare industries and hospitals. Curse of dimensionality is a barrier to extracting useful information from patient survey data which can help in the treatment and care of patients. It is paramount to have methods to find importance of features based on such huge volumes of stored information for the desired outputs. The health-related quality of life (HRQOL) is a powerful paradigm to help reaching such a desired output, measuring as patient satisfaction. In such scenarios, it is difficult to investigate the features, out of such high-dimensional data, that could best represent desired output and explain them so that such features can be used in the future at the point f care. In this paper we propose a Cluster-based Random Forest (CB-RF) method to particularly exploit the most important features for the desired output, which is Expanded Prostate Index Composite-26 (EPIC-26) domain scores. EPIC-26 is being used for assessing a range of HRQOL issues related to the diagnosis and treatment of prostate cancer. Different feature extraction methods are applied to extract features and the best method is the proposed CB-RF model which could find the most important features (10 or less) out of over 1500 features that can be used to accurately estimate patient with their EPIC-26 values with on average 85% coefficient of correlation between predicted and observed values of real dataset including 5093 patients.