HEALTHCARE PREDICTIVE MODEL BASED ON BIG DATA FUSION FROM BIOMEDICAL SENSORS

The paper presents a method for analysing 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 (temperature and humidity). This paper presents the software application VitalMon developed for sensors data tracking and a decision tree method for predictive health modelling based on data mining. Based on this method can be developed a decision support system for healthcare. Also this method, decision tree, can be used in healthcare predictive modelling 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.