25th Mar, 2020 Read time 2 minutes

3 different predictive analytics models used in safety

The field of predictive analytics is versatile, and new models are constantly being developed to provide different forecasting information to safety decision-makers. The various models all have their strengths and weaknesses and are more applicable in some industries than they are in others. Here are some of the top predictive analytics models that have health and safety applications.

The forecast model

This model of predictive analytics focuses on specific metric value prediction with the model learning from historical data and generating new forecast data. Any safety situation in which historical data can be processed can make use of this predictive analytics model. For example, a safety inspector or manager could predict the amount of missed or faulted risk assessments that may occur within a specific period.


The classification model

This is one of the most widely used and simplest forms of predictive analytics. The fundamental principle behind it is that it classifies the data that you generate from the input into your model. From a safety perspective, this could concern, for example, the likelihood that a specific piece of equipment may be faulty in a certain timeframe.


The outliers model

The outliers model is particularly useful in health and safety as it identifies anomalous data within a series. Not only can it categories information, but if the input data is correct it can identify anomalies which may indicate a specific area where a safety concern needs to be addressed.

Take, for example, a series of factories or plants in which incidents are recorded over time, if a spike is recorded during a certain period, an outliers model could identify the data in addition to potential anomalies in parameters (like air quality) which may be present in different factories.


The fundamental differences between machine learning and deep learning

For large sets of data to be processed with these models, a certain degree of artificial intelligence is needed. This is where machine and deep learning come in.

Machine learning in predictive analytics concerns the creation of algorithms which can be changed and modified without human intervention. This is a common method of processing data in a predictive manner.

Deep learning is more in-depth than machine learning and involves a similar process except with additional layers of algorithms which each provide different interpretations of the data provided.


Getting the most out of predictive models in health and safety

Whilst it is not necessary to have a deep understanding of the algorithms that function in the background of predictive analytics, having an understanding of the data that should go into a model and how to develop actions on the data that comes out of it is. If you identify problem areas in your companies safety through predictive analytics, make sure you make changes and measure their effectiveness going forward.

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