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Predictive analytics

Predictive analytics

Predictive analytics is a branch of statistical techniques from data mining, predictive modeling, and machine learning; these techniques analyze current and historical facts to make predictions about future or otherwise unknown events.

Predictive analytics is a branch of advanced analytics used to make predictions about unknown future events. Predictive analytics uses techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions. Those predictions are based on patterns found in historical and transactional data in order to identify possible opportunities, trends, and risks in the future. Through predictive analytics, organizations work to increase their revenue, increase or create a competitive advantage, and develop insights to improve economic performance and develop organizational differentiation.

Predictive analytics development workflow
An example of a predictive analytics workflow.

In an example, such as energy load forecasting used to predict energy demand, the workflow to develop a predictive analytics model uses vast amounts of data from energy producers, grid operators, historical load data, and energy traders. The data is then cleaned to remove outliers, data spikes, missing data, or anomalous points to create a unified energy load, temperature, and dew point. Then the data is used to develop an accurate predictive model using statistics, curve fitting tools, or machine learning in order to build and train a predictive model and a related neural network. New data can then be used to test how well a predictive model performs. Finally, the model can be integrated into a load forecasting system in a production environment to understand the accuracy of the model, and once tuned, can be used in production and forecasting systems.

Predictive analytics models

Predictive analytics are not a monolith; there are different models developed for design-specific functions. These include forecast models, classification models, outlier models, time series models, and clustering models.

Predictive analytic model types


Classification models

These models are a common predictive analytics model that work by categorizing information based on historical data. These models are used in different industries because they can easily be retrained with new data and provide a broad analysis for answering questions. Classification models are often used in industries like finance and retail.

Clustering models

These models take data and sort it into different groups based on common attributes. The ability to divide data into different datasets based on specific attributes is useful in specific applications, like marketing, where marketers can divide a potential customer base based on common attributes.

Forecast models

These models are one of the most common predictive analytics models, which handle metric value predictions by estimating the values of new data based on learnings from historical data. They are often used to generate numerical values in historical data when there is none to be found. One of the strengths of forecast models is the ability to input multiple parameters.

Outliers models

These models, unlike classification and forecast models that work with historical data, work with anomalous data within a data set and by identifying the unusual data in isolation or in relation with different categories. These models are often useful in industries where identifying models are effective in detecting fraud based on their abilities to find anomalies. And since an incidence of fraud is a deviation from the norm, the outlier model can likely predict fraud before it occurs; in the case of financial or transactional fraud, it can assess the amount of money lost, location, purchase history, time, and the nature of the purchase.

Time series model

This is a model that focuses on data in which time is the input parameter. The time series model works by using different data points to develop a numerical metric that predicts trends within a specified period. This model can be used to see how particular variables as they can change over time.

Use cases

Predictive models are used for all kinds of applications, including weather forecasting, creating challenging and engaging video games, and translating voice to text for mobile phone messaging. These applications use statistical models of existing data to make predictions about future data. Descriptive models can determine relationships, patterns, and structures in data that can draw conclusions about how changes in the underlying processes that generate the data will change the results. Predictive models build on these descriptive models to determine the likelihood of certain future outcomes given current conditions or a set of expected future conditions. Predictive models have been used in industries such as finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing.

Predictive analytics use cases

Use case


Especially when used in manufacturing and utilization of resources in the supply chain, predictive modeling can be used to clean and optimize the quality of data used for forecasting in inventory management and supply chain optimization.

Fraud detection

Combining analytics methods can improve pattern detection and prevent criminal behavior. Behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities, and persistent threats.

Marketing campaigns

Predictive analytics is used to determine customer responses or purchases, as well as promote cross-sell opportunities, and help businesses attract, retain, and grow profitable customers.

Operational efficiency

Companies can use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue, and predictive analytics can enable organizations to function more efficiently.

Predictive maintenance

Predictive analytics can be used for organizations to predict when routine equipment maintenance will be required and can be scheduled before a problem or malfunction arises.

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Predictive analytics and its application has in some cases been criticized and legally restricted. This has largely been in cases where the use of predictive analytics has resulted in perceived inequities of its outcomes. This has most commonly been in situations when predictive models have resulted in statistical discrimination against racial or ethnic groups in areas such as credit scoring, home lending, employment, or risk of criminal behavior. A famous, and illegal, example of this is the practice of redlining in home lending by banks. Because of incidents and practices such as redlining, any predictive analytics models that include information such as a person's race are now often excluded from predictive analytics.

Predictive analytics companies


Much of medicine and healthcare is about anticipating and reducing risk based on current and historical patient data. Clinicians are required to make decisions without absolute certainty, but with the advance of predictive analytics in healthcare, these decisions offer the promise of being better performed than previously. Built upon the growing sophistication of big data analytics capabilities, predictive analytics can take the patterns in historical data for predictive outcomes and allow clinicians to alert patients about possible health concerns in the near future. This can be important in cases such as intensive care, surgery, or emergency care where quick reactions and sensitivity to something wrong can save lives.

Predictive analytics can help answer questions about the best treatment for a patient, the likelihood of a patient to experience adverse events following a given procedure, and the likelihood that the patient has a given disease. This can be used at various points in the patient journey:

  • Diagnosis—predictive analytics have been used to predict malignant mesothelioma diagnosis in a patient cohort. Patients diagnosed early can start treatment sooner and improve their overall chances for survival.
  • Prognosis—researchers have used predictive analytics on physiological data from patients with congestive heart failure to predict which patients were at greatest risk of readmission following a hospital stay. Using that information, physicians could implement interventions early to prevent the predicted readmissions.
  • Treatment—clinicians have used machine learning-based predictive analytical models to determine the most effective course of treatment for chronic pain patients.

Using artificial intelligence and machine learning, predictive models can intake huge amounts of diverse data for a patient and forecast a patient's response to certain treatments or devices, their risk of developing a specific disease, and their prognosis for a given condition. Predictive analytics can also offer a chance to develop personalized healthcare, where the treatment of a patient can be developed from the individual's medical history, environment, social risk factors, genetics, and unique biochemistry, among other characteristics. The key to personalized healthcare is treating a patient based on their specific attributes, instead of relying on population averages that do not serve all patients. This can also push healthcare towards treating a patient as an individual rather than an average and improve overall patient care.

Furthermore, once a patient is being treated, predictive analytics can alert clinicians and caregivers of the likelihood of events and outcomes before they occur, helping healthcare professionals prevent as much as cure health issues. Driven by artificial intelligence and data derived from the Internet of Things (IoT) device monitoring patients, algorithms fed with historical and real-time data can make meaningful predictions. Such predictive algorithms can be used to support clinical decision making for an individual patient, and to inform interventions on a cohort or population level. This can also be applied to hospitals operational and administrative challenges.

Ways predictive analytics is being used in healthcare

Use case

Avoiding hospital readmissions

With hospitals in the United States subject to penalties under Medicare's Hospital Readmissions Reduction program (HRRP), adding a financial incentive to preventing unplanned returns to the inpatient setting could be achieved with predictive analytics. This includes predictive analytics being used to improve transitions of care and deploying care coordination strategies to warning a healthcare provider when a patient's risk factor indicate a high likelihood for readmission. Analytics can capture data, such as a C. difficile infection or vital sign instability on discharge, which have been shown to increase the chance of readmissions, and identify patients with those traits to improve the level of care and reduce readmissions. Further, these tools can help design discharge planning protocols to further prevent readmissions.

Developing precision medicine and new therapies

As precision medicine and genomics increase, providers and researchers are turning to analytics to supplement traditional clinical trials and drug discovery techniques. This offers a new way to reduce the need to recruit patients for complex and costly clinical trials while speeding up the evaluation of new therapies. This can also help as the ability for researchers to develop individualized drug increases, predictive analytics can provide better understanding to see aspects of individual physiology and genetics in drug metabolizing enzymes is being used to identify patient subgroups that need dose adjustments. And these organizations can use modeling and simulation to predict clinical outcomes, inform clinical trial designs, support evidence of effectiveness, optimize dosing, predict product sfaety and evaluate potential adverse event machenisms.

Ensuring strong data security

Predictive analytics and artificial intelligence can also play an important role in cybersecurity. This can enable hospitals and healthcare facilities monitor patterns in data access, sharing, and utilization and give those organizations early warnings when something changes, especially if those changes indicate an intruder may have penetrated the network.

Forestalling appointment no-shows

Gaps and no-shows can have financial ramifications for healthcare providers, and using predictive analytics can identify patients likely to skip an appointment without advanced notice to improve provider satisfaction, cut down on revenue losses, and give organizations the opportunity to offer open slots to other patients, thereby increasing speedy access to care.

Getting ahead of patient deterioration

While in hospital, a patient can face a number of potential threats to their wellbeing, including the development of sepsis, hard to treat infections, or a sudden downturn due to existing clinical conditions. Predictive analytics can take data from a patient and their vitals and may be able to identify the deterioration before symptoms manifest themselves. Machine learning strategies can also be used to predict clinical events, such as the development of acute kidney injury. The University of Pennsylvania has developed a predictive analytics tool leverageing machine learning and EHR data to identify patients on track for severe sepsis or septic shock 12 hours before the onset of the condition.

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Examples of predictive analytics in healthcare

As AI and machine learning has been developed for predictive analytics in healthcare, there have been some examples of predictive analytics used in studies for understanding the possible applications. Some of these were done during the COVID-19 pandemic, including:

  • COViage, a software prediction system, assessing whether hospitalized COVID-19 patients are at a high risk of needing intubation.
  • CLEWICU System, a prediction software that works to identify which ICU COVID-19 patients are at risk for respiratory failure or low blood pressure.
  • Mount Sinai Health System's AI model that analyzes computed tomography (CT) scans of the chest with patient data to rapidly detect COVID-19.
  • Researchers at the University of Minnesota, along with Epic Systems and M Health Fairview, developed an AI tool capable of evaluating chest x-rays to diagnose a possible case of COVID-19.

Previous to COVID-19, other examples of some uses of predictive analytics in healthcare included:

  • The University of Pennsylvania has developed a predictive analytics tool that uses machine learning and EHR data to identify patients on track for severe sepsis or septic shock twelve hours before the onset of the condition.
  • A predictive model developed in a study by Duke University saw that clinic-level data could capture an additional 4800 patient no-shows per year with higher accuracy than previous attempts to forecast patient patterns.
  • UnityPoint Health, a network of healthcare facilities, aggregated answers to a questionnaire of why patients were being readmitted to develop a predictive model was able to assign a readmission risk to every visiting patient.
  • Diabetes Care published a study demonstrating that predictive analytics models for healthcare can determine a five to ten years life expectancy for older adults with diabetes and allowed doctors to craft treatment plans for individual patients.
  • A research team at Vanderbilt University Medical Center (VUMC) developed a predictive analytics model using patients' EHR to forecast the likelihood of suicide attempts by particular patients. Through an eleven month testing period, the patients were classified into eight groups based on their risk factor, of which the highest-risk group accounted for over 33 percent of suicide attempts.
FDA acceptance for predictive analytics in healthcare

The FDA pathway for medical devices using artificial intelligence (AI) and machine learning (ML) for medical decision making and data analysis is stringent, with difficult regulatory requirements for medical device licensing. This process is rigorous and time and resource consuming and has been considered a pivotal barrier in the introduction of AI and ML in medicine. Before any medical hardware or software are made legally available, the parent company has to submit it to the FDA for evaluation. For medically oriented AI and ML-based algorithms, the regulatory body has three levels of clearance: 510(k), premarket approval, and the de novo pathway, each which has specific required criteria.

Types of FDA approvals for AI/ML-based medical technology

Level of FDA clearance

510(k) clearance

A 510(k) clearance for an algorithm is granted when it has been shown to be at least as safe and effective as another similar, legally marketed algorithm. The submitter seeking this clearance must provide substantial proof of equivalence in their application. Without an approval of being substantially equivalent to the other algorithm, the one pending approval cannot be legally marketed.

de novo pathway

Regarding the de novo classification, it is used to classify those novel medical devices for which there are no legally marketed counterparts, but which offer adequate safety and effectiveness with general controls. The FDA performs a risk-based assessment of the device in question before approval and allowing the device to be marketed.

Premarket approval

Premarket approval is issued to algorithms for Class III medical devices. The latter are those that can have a large impact on human health and as such, their evaluation undergo more thorough scientific and regulatory processes to determine their safety and effectiveness. In order to approve an application, the FDA determines that the device's safety and effectiveness is supported by satisfactory scientific evidence. Upon approval, the applicant can proceed with marketing the product.

In the case where companies work to update the algorithm with a product, the FDA considers the update as a new product and requires the update to receive approval in the same way as the original product. There has been a realization on behalf of the FDA that the process might be impossible to maintain, so the FDA has begun to consider a total product lifecycle-based regulatory framework. This framework would allow for modifications to be made from real-world learning and adaptation, while still ensuring that the safety and effectiveness of the software as a medical device are maintained.

While the FDA considers the possibility of changing the process for approving medical devices using predictive analytics, the FDA has cleared or approved several medical devices using "locked" algorithms. The "locked" algorithm has been defined as an algorithm that defines the same result each time the same input is applied to it and does not change. But the promise for medical devices using algorithms is that they will adapt over time to improve in accuracy and potential applications. These are described by the FDA as adaptive algorithms, and there is no regulatory framework designed for them.

An attempt to change the FDA's proposed regulatory framework from 2019 that elaborates on potential approaches to premarket review for AI and ML-based software modifications. The FDA has also recognized that the adaptive algorithms require a total product lifecycle regulatory approach (TPLC), enabling a rapid cycle of product improvement while maintaining effective safeguards. This TPLC approach is based on the Digital Health Software Precertification (Pre-Cert) Program, allowing for the evaluation of software as a medical device (SaMD) products throughout the lifecycle.

AI/ML-based medical technologies receiving FDA approval

Type of FDA approval
Year of approval


Acute intracranial hemorrhage triage algorithm

510(k) premarket notification


Advanced Intelligent Clear-IQ Engine

Noise reduction algorithm

510(k) premarket notification


AI-ECG Platform

ECG analysis support

510(k) premarket notification


AI-Rad Companion (Cardiovascular)

CT image reconstruction - cardiovascular

510(k) premarket notification


AI-Rad Companion (Pulmonary)

CT image reconstruction - pulmonary

510(k) premarket notification


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Companies developing healthcare products using predictive analytics

Venture capital firms investing in predictive analytics for healthcare companies




Further reading


How different predictive analytics models work | Selerity


December 12, 2019

Machine Learning Vs. Predictive Analytics: Which Is Better For Business?

Eric Vardon


June 12, 2020

Predictive Analytics Definition

Clay Halton


May 5, 2021

Predictive analytics: Transforming data into future insights

John Edwards


August 16, 2019

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Documentaries, videos and podcasts





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