2016 » Papers » Volume 1 » Healthcare predictive models based on big data fusion from biomedical sensors
|1. HEALTHCARE PREDICTIVE MODELS BASED ON BIG DATA FUSION FROM BIOMEDICAL SENSORS|
Volume 1 | DOI: 10.12753/2066-026X-16-046 | Pages: 328-333
The paper presents a method for analyzing data from sensors and developing the predictive models based on learning methods. There are some methods, described on scientific literature, such as statistical methods (linear regression, logistic regression, and Bayesian models), advanced methods based on machine learning and data mining (decision trees and artificial neural networks) and survival models. All of these methods are intended to discover the correlation and covariance between biomedical parameters.
This paper presents the decision tree method for predictive health modeling based on machine learning and data mining. Based on this method used can be developed a decision support system for healthcare. Machine learning is used in healthcare predictive modeling for learning to recognize complex patterns within big data received from biomedical sensors.
The sensors data fusion refers to the usage of the sensors wireless network and data fusion on the same level (for similar sensors - e. g. temperature sensors) and on different levels (different sensors category - pulse, breath, temperature, moisture sensors) for developing the decision systems.
Big data concept is familiar for medical sciences (genomics, biomedical research) and also for physical sciences (meteorology, physics and chemistry), financial institutions (banking and capital markets) and government (defense).
For predictive models in clinical analysis is important to establish the time steps discretization of occurrence of a particular event (critical state required continuous monitoring) for observe the impact of the correlated values for biomedical parameters. These aspects presented are useful for healthcare learning about correlation between diseases and biomedical parameters.
biomedical, sensors, fusion, big data, machine learning, data mining, predictive models, healthcare, wireless, monitoring, discretization