Online Incremental Learning Algorithm for anomaly detection and prediction in health care

Anomaly Detection in health care by monitoring the vital health parameters of patients is a challenging problem in machine learning. The existing algorithms do not process the data incrementally and hence are not very effective in predicting the anomalies accurately and at the correct instance. In t...

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Bibliographic Details
Published in2014 International Conference on Recent Trends in Information Technology pp. 1 - 6
Main Authors Raghuraman, Kirthanaa, Senthurpandian, Monisha, Shanmugasundaram, Monisha, Bhargavi, Vaidehi, V.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2014
Subjects
Anomaly Detection
Artificial Intelligence
Blood pressure
Health Care Monitoring
Heart rate
Kalman filters
Machine Learning
Medical services
Prediction algorithms
Regression
Sensitivity
Online AccessGet full text
DOI10.1109/ICRTIT.2014.6996092

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Summary:Anomaly Detection in health care by monitoring the vital health parameters of patients is a challenging problem in machine learning. The existing algorithms do not process the data incrementally and hence are not very effective in predicting the anomalies accurately and at the correct instance. In this paper, in order to process the health data in an online fashion a novel Online Incremental Learning Algorithm (OILA) is proposed. The OILA predicts the health parameters using a regression based approach with a feedback mechanism to reduce error. An alert is generated when an anomaly is seen in the health parameters, thus alerting the doctor to be cautious. The algorithm is compared with Kalman Filter for comparing the prediction capabilities of OILA with Kalman Filter. The proposed algorithm is validated with real time health parameter data sets for health parameters namely heart rate and blood pressure.
DOI:10.1109/ICRTIT.2014.6996092