Predictive modeling is the phenomenon of creating, testing, and validating a model to predict the probability of an outcome in the best possible way. The main objective of predictive modeling is to anticipate an event, behavior, or output through the use of various predictors. But before going into the predictive modeling techniques, let’s first decode predictive modeling in simpler terms.
What is predictive modeling?
Predictive modeling is a process where data mining and probability are used to forecast outcomes. Each model in this technique is made up of a number of predictors or variables that influence results in the future. After the collection of data for relevant predictors, a statistical model is created. The model may be a simple linear equation or a complex neural network and this is mapped out by sophisticated software. After the availability of additional data, the statistical analysis model is revised or validated. Simply put, predictive modeling techniques involve artificial intelligence to create a profile of predictions (algorithms) from past individuals. The model that is formulated is then deployed so that any new individual can get an instant prediction for his need, whether a bank loan or an accurate medical diagnosis. Given below is a list of few popular predictive modeling techniques.
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Predictive Modeling Techniques
When there is an abundance of data, then regression algorithms might help to predict trends in the future based upon that data. For example, if any raw material is being purchased for the manufacturing process, then the monthly price data can be collected to forecast seasonal fluctuations in those prices.
Clustering algorithms help find the relations between variants, which are not easily visible. Product relationships can also be discovered using this technique. This technique is very useful in bundling services and upselling purposes.
This is one of the predictive modeling techniques which is considered to the best for successful predictive analytics. In this technique, a mechanism is formulated to collect all the output from various models. The information fetched is then used for final analysis. Each model has different specifics like decision trees, queries, scenarios, etc. Therefore, it is very essential to understand what works well for your data and the problems you are targeting.
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Predictive Modeling in Healthcare
For improving the quality of healthcare, minimizing costs and improving the health of the population, the use of predictive modeling in healthcare becomes quintessential. Its role in healthcare can be listed as:
- It has immense potential to drive future models of healthcare and is a key step towards personalized medicines
- Assimilating domain knowledge and real-world evidence to address the issues of data quality is one of the most important roles of predictive modeling in healthcare
- Modeling techniques like feature selection and smart ensemble methods can be used to predict and improve patients’ risk of infection, length of stay, utilization of services, the risk of readmission, and many others
- Modeling technique in healthcare can help in harmonizing data elements across data collection systems
- Modeling technique in healthcare helps in automatically mining the data and examining the strength of the relationships among variables to obtain the desired outcome.