Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster

Health care systems are merely designed to meet the needs of increasing population globally. People around the globe are affected with different types of deadliest diseases. Among the different types of commonly existing diseases, diabetes is a major cause of blindness, kidney failure, heart attacks...

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Bibliographic Details
Published in:Cluster computing Vol. 22; no. Suppl 1; pp. 1 - 9
Main Authors: Yuvaraj, N., SriPreethaa, K. R.
Format: Journal Article
Language:English
Published: New York Springer US 01.01.2019
Springer Nature B.V
Subjects:
Accuracy
Algorithms
Big Data
Chronic illnesses
Classification
Cloud computing
Clusters
Computer Communication Networks
Computer networks
Computer Science
Data mining
Datasets
Decision trees
Diabetes
Disease prevention
Distributed processing
Feature selection
Glucose
Health care
Health care policy
Health services
Insulin
Machine learning
Operating Systems
Patients
Processor Architectures
Signs and symptoms
United States > US
Hadoop clusters
Predictive analysis
Machine learning algorithms
Healthcare systems
Cluster computing
ISSN:1386-7857, 1573-7543
Online Access:Get full text
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Summary:Health care systems are merely designed to meet the needs of increasing population globally. People around the globe are affected with different types of deadliest diseases. Among the different types of commonly existing diseases, diabetes is a major cause of blindness, kidney failure, heart attacks, etc. Health care monitoring systems for different diseases and symptoms are available all around the world. The rapid development in the fields of Information and Communication Technologies made remarkable improvements in health care systems. Various Machine Learning algorithms are proposed which automates the working model of health care systems and enhances the accuracy of disease prediction. Hadoop cluster based distributed computing framework supports in efficient processing and storing of extremely large datasets in cloud environment. This work proposes the novel implementation of machine learning algorithms in hadoop based clusters for diabetes prediction. The results show that the machine learning algorithms can able to produce highly accurate diabetes predictive healthcare systems. Pima Indians Diabetes Database from National Institute of Diabetes and Digestive Diseases is used to evaluate the working of algorithm.
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-017-1532-x