**Predictive analytics is defined as the process of using data analysis to make predictions based on that information. **

Health care systems will use predictive analysis to also support decisions made in different care centers. Healthcare analysts use predictive analytics to "prevent patient illness, avoid penalties, and reduce costs"

**Bayesian Analysis -**We use it in our computer vision and pattern recognition projects. In these methods, parameters are treated as random variables and probability is defined as degrees of belief. The probability of an event occurring is based on the degree to which, without being certain, you believe that the event is true or false with the evidence that is known. That is, we start from a defined belief in the probabilistic distribution of an unknown parameter and as we acquire new data, that degree of belief is updated.**Time Series and Data Mining -**We are based on the study of historical data, we collect data from time intervals such as time spent in a certain room in the house, times to go to the bathroom, time spent sitting, hours of sleep, etc. . In this way we can identify patterns that are constantly repeating. We use this method for the surveillance and security of elderly people who live alone.**Ensemble Models -**We use it for the prediction of needs in people with severe motor disabilities, cases of cerebral palsy. It is the way to build highly accurate predictive models. Through the bagging and boosting algorithms we can achieve an impressive level of precision. This technique consists of building a new model by training several similar models combining the results to improve precision, reduce biases, reduce variance, and identify the best model to use with new data.

HealthcareIn healthcare, predictive analysis is used to determine which patients are at risk of developing certain disorders such as diabetes, asthma or cardiovascular diseases among other recurrent diseases.

The study of infectious diseases is often based on mathematical epidemiological models that attempt to emulate the dynamics of the disease and estimate the parameters related to it, such as the reproducibility rate, the mortality rate, and so on.

The basic form of this type of simulation is SIR models based on the assumption that the population can be classified into three independent compartmentalized groups (susceptible, infected and recovered person). The number and type of compartmentalized groups can be modified to better reflect disease-specific dynamics, as in SEIR (susceptible, exposed, infected, and recovered) models. The models study how individuals can progress from one compartmentalized group to the next.

In the adjustment, we determine which mathematical functions and parameter values are compatible with the data that we observe on the daily evolution of the epidemic in the past, also incorporating biomedical knowledge about the natural history of the disease, in a Bayesian approach. Once the model that best fits the data from the past has been chosen, the one that best predicts it is used to simulate or predict the evolution of cases in the future, under different intervention scenarios.