What Is Predictive Modeling In Healthcare?

What Is Predictive Modeling In Healthcare
Inga Shugalo – Healthcare Analyst With the major part of the US healthcare spending going towards a small share of the US population, healthcare providers have a serious issue on their hands. In 2019, 5% of Americans accounted for half of the overall healthcare expenditures, Peterson-KFF reports.

The main reason for this disproportion is the lack of focus on patients with chronic conditions, which results in ill-timed interventions and increased readmission rates. While there is no shortage of data or solutions that can be applied to solving this challenge, the hardest part is making this data actionable.

However, a recent economic study by Medtronic has proven that predictive analytics and modeling could help a median-sized US hospital save over $500K annually by delivering actionable insights automatically. This is why more and more medical providers are turning to and predictive modeling to promote disease prevention and consequently reduce healthcare expenses. Each model is created with a range of predictor variables that affect future results. The model can use a simple linear equation or a complex neural network to perform advanced tasks. Data analysts run and assess different models to solve the defined problem.

After the assessment, the model with the highest score is validated, tested, and run against a real-world dataset.
Here is an example of how predictive modeling assists physicians: a patient of certain race, age, medical history and genetics presents herself at a hospital with a newly diagnosed condition.

The predictive model considers the new condition and all the patient-specific data mentioned above, while looking at the population cohort with similar characteristics to develop a treatment plan tailored to this particular patient. This plan is then presented to the attending physician for approval, exemplifying the use of,

Employing predictive modeling helps doctors, support staff, and financial departments receive alerts about potential outcomes and risks, so that they are better prepared for the future.
Below are some predictive modeling examples in healthcare.
According to Medicare’s Hospital Readmission Reduction program, healthcare organizations are subject to penalties in case of patient readmission within 30 days after receiving care.

Therefore, avoiding readmission will not only improve patient satisfaction but also save providers money. Predictive analytics can identify patients with a high risk of readmission after a particular treatment. can explain what exactly puts patients into this high-risk group and advise doctors on the time of a follow-up visit and optimal discharge instructions to avoid readmission.

Additionally, predictive analytics can incorporate social factors determining a patient’s health.
When the social factors are considered, it becomes easier to understand when patients might not adhere to their prescribed care plans.
Having challenges makes it much harder for people to be able to properly focus on their health.

The science of predictive analytics is beginning to develop algorithms to track who these individuals may be and respond with carefully customized ways of reaching them to help them on their health journeys. Rich Temple Vice President, Chief Information Officer at Deborah Heart and Lung Center UnityPoint Health, a network of healthcare organizations based in Iowa, has reduced readmissions by 40% within 1.5 years of using predictive analytics. In one example, their home health team used predictive analytics to understand which patients became most vulnerable when the city was hit by a blizzard.

This helped them focus their attention on particular individuals and reduce hospital visits. Identifying patients who are likely to harm themselves gives the opportunity to offer mental health care, which can hopefully prevent events of self-harm and suicide. Kaiser Permanente together with the Mental Health Research Network used predictive modeling along with the EHR and a standard questionnaire on depression to identify patients who were at high risk of attempting suicide.

They have discovered that suicide attempts were 200 times more likely among the 1% of patients flagged as high-risk by the algorithm. It should be noted that applying predictive modeling requires larger computational capacity, so the company should have performed before implementing the technology.

Medical researchers are turning to predictive modeling and analytics to supplement the traditional clinical processes.
One interesting usage is adoption of predictive modeling for “in silico” testing.
It involves importing an extensive number of patient-specific geometrics to a computer and using those for modeling the impact of therapies on patients.

This is a promising way to evaluate new therapies without the need to recruit patients. This method is now used in clinial trial management software for trial simulations related to degenerative conditions such as Alzheimer and Parkinson’s disease. FDA’s Center for Drug Evaluation and Research (CDER) is currently using modeling and simulation to predict clinical outcomes, inform clinical trial designs, support evidence of effectiveness, optimize dosing, predict product safety, and evaluate potential adverse event mechanisms. Scott Gottlieb MD, Former Food and Drug Administration (FDA) Commissioner Another common use case for predictive modeling in healthcare is clinical decision support, where predictive models are applied to help clinicians make better decisions about patient care.

Predictive models help identify patients who are at risk of developing certain conditions and then recommend treatment plans and predict their outcomes. These tools can also help doctors identify patients who may have a risk of adverse drug reactions. As chronic diseases are the key reasons for exorbitant healthcare spending, providers strive to prevent the unplanned deterioration of patients with chronic conditions.

Applying predictive modeling, doctors from Intermountain Healthcare, Salt Lake City, Utah, managed to assign risk scores to their patients with chronic obstructive pulmonary disease (COPD). The risk scores called LIVE took into account blood tests and an array of parameters that could signal other conditions in COPD patients.

Such a detailed patient profile allowed clinicians to make timely and grounded decisions regarding patient treatment, hospitalizations, transferring to palliative care, and allocating wards and resources, thus improving the quality of care.
Due to the size and complexity of modern hospitals and their services, hospital inventory management has grown more complex.

But, clinicians can discover certain trends in resource allocations and foresee the facility’s upcoming needs. This can enable healthcare administrators to buy the required medical goods or relocate them right on time to prevent stockouts. Besides, hospitals can employ predictive analytics for determining what resources are prone to be out-of-stock faster than others relying on trends among the population or seasonal needs.

With the help of predictive modeling tools in healthcare, healthcare organizations can better understand their intricate supply requirements and find ways to save money and reduce waste. Predictive analytics offers healthcare providers a better view of patients and their needs. Community Health Network from Indianapolis, Indiana, provides one of the best examples of predictive modeling in healthcare with the clinic managing to reduce the level of no-shows using a smart predictive algorithm.

To have no-show risk patients confirm their appointment, the team employed a three-step notification process:

A text message to confirm the visit
A personalized outreach
A call from administrators

A technology of this kind can predict patients’ behavior in terms of following their treatments. It can also pinpoint which interventions or healthcare messages would resonate with certain patients or patient populations. With the help of this information, providers can design, upscale the efficiency of their work with patients, and improve their health outcomes.

What is predictive modeling with example?

Predictive modeling is a technique that uses mathematical and computational methods to predict an event or outcome. A mathematical approach uses an equation-based model that describes the phenomenon under consideration. The model is used to forecast an outcome at some future state or time based upon changes to the model inputs.

The model parameters help explain how model inputs influence the outcome.
Examples include time-series regression models for predicting airline traffic volume or predicting fuel efficiency based on a linear regression model of engine speed versus load.
The computational predictive modeling approach differs from the mathematical approach because it relies on models that are not easy to explain in equation form and often require simulation techniques to create a prediction.

This approach is often called “black box” predictive modeling because the model structure does not provide insight into the factors that map model input to outcome. Examples include using neural networks to predict which winery a glass of wine originated from or bagged decision trees for predicting the credit rating of a borrower.

What do you mean by predictive Modelling?

Predictive modeling is a commonly used statistical technique to predict future behavior. Predictive modeling solutions are a form of data-mining technology that works by analyzing historical and current data and generating a model to help predict future outcomes.

In predictive modeling, data is collected, a statistical model is formulated, predictions are made, and the model is validated (or revised) as additional data becomes available. For example, risk models can be created to combine member information in complex ways with demographic and lifestyle information from external sources to improve underwriting accuracy.

Predictive models analyze past performance to assess how likely a customer is to exhibit a specific behavior in the future. This category also encompasses models that seek out subtle data patterns to answer questions about customer performance, such as fraud detection models.

What is predictive modeling in medical imaging?

What Is Predictive Modeling in Healthcare? – Predictive modeling (sometimes called predictive analytics) deals with statistical methods, data mining, and game theory to analyze current and historical data collected at the medical establishment. These data help to improve patient care and ensure favorable health outcomes.

Based on medical record information, age, social and economic characteristics, individual anatomy, and many other factors, predictive analysis can reveal patients’ susceptibility to such diseases as diabetes, asthma, and other lifestyle-related conditions.
When building a predictive model, data analytics is used for finding similar patterns in behavior and forecasting people’s responses or actions to occurring events.

The world’s largest medical organizations are on their way to integrating advanced practices such as predictive analysis, simulation, and variable modeling. The end goal with using these practices is the optimization of decision-making, problem-solving, identifying opportunities for improving the health system.

What are the benefits of predictive models in healthcare?

What is predictive analytics in healthcare exactly? – Predictive analytics in healthcare is a process of analyzing historical healthcare data to identify patterns and trends that may be predictive of future events. Predictive analytics in healthcare can be used to predict the likelihood of particular health conditions, clinical decisions, trends, and even spread of diseases.

By using predictive analytics in healthcare, providers can make more informed decisions about which treatments to offer patients and how best to tailor those treatments to individual needs.
Predictive healthcare analytics can also help to identify patients who are at risk for complication or relapse and provide interventions before problems occur.

Overall, predictive analytics has the potential to improve the quality and efficiency of healthcare delivery. Predictive analytics has become an invaluable tool for healthcare organizations, as it helps them make better-informed decisions and uncover hidden opportunities in their data.

Data and analytics technology is used to detect patterns and trends in order to make predictions about patient outcomes, improve care quality, reduce healthcare costs, increase efficiency, and more.
Physicians can use predictive analytics to diagnose diseases accurately and quickly, while hospitals can use it to identify high-risk patients or forecast the need for resources.

Insurance companies have also adopted predictive analytics models to gain a better understanding of customer behavior. Leveraging a predictive model, healthcare organizations are able to make smarter decisions that benefit both patients and providers alike.

With a predictive analytics model, the healthcare industry is better equipped to address some of its most pressing challenges. It is an invaluable tool in the fight against disease and a key factor in improving patient outcomes. By taking advantage of a predictive analytics tool, healthcare organizations can ensure they are making the most of their data and resources to provide the best care possible.

This will not only improve patient outcomes but also help reduce costs for patients and providers alike. With predictive analytics, healthcare has become more efficient, reliable, and cost-effective than ever before. From better diagnoses to improved resource management, predictive analytics is helping make the healthcare industry more efficient and effective.

What are the two types of predictive modelling?

How does predictive analytics work? – Predictive analytics is driven by predictive modelling. It’s more of an approach than a process. Predictive analytics and machine learning go hand-in-hand, as predictive models typically include a machine learning algorithm.

These models can be trained over time to respond to new data or values, delivering the results the business needs. Predictive modelling largely overlaps with the field of machine learning, There are two types of predictive models. They are Classification models, that predict class membership, and Regression models that predict a number.

These models are then made up of algorithms. The algorithms perform the data mining and statistical analysis, determining trends and patterns in data. Predictive analytics software solutions will have built in algorithms that can be used to make predictive models.

Decision trees: Decision trees are a simple, but powerful form of multiple variable analysis. They are produced by algorithms that identify various ways of splitting data into branch-like segments. Decision trees partition data into subsets based on categories of input variables, helping you to understand someone’s path of decisions.
Regression (linear and logistic) Regression is one of the most popular methods in statistics. Regression analysis estimates relationships among variables, finding key patterns in large and diverse data sets and how they relate to each other.
Neural networks Patterned after the operation of neuronsin the human brain, neural networks (also called artificial neural networks) are a variety of deep learning technologies. They’re typically used to solve complex pattern recognition problems – and are incredibly useful for analysing large data sets. They are great at handling nonlinear relationships in data – and work well when certain variables are unknown

Other classifiers:

Time Series Algorithms: Time series algorithms sequentially plot data and are useful for forecasting continuous values over time.
Clustering Algorithms: Clustering algorithms organise data into groups whose members are similar.
Outlier Detection Algorithms: Outlier detection algorithms focus on anomaly detection, identifying items, events or observations that do not conform to an expected pattern or standard within a data set.
Ensemble Models: Ensemble models use multiple machine learnin g algorithms to obtain better predictive performance than what could be obtained from one algorithm alone.
Factor Analysis: Factor analysis is a method used to describe variability and aims to find independent latent variables.
Naïve Bayes: The Naïve Bayes classifier allows us to predict a class/category based on a given set of features, using probability.
Support vector machines: Support vector machines are supervised machine learning techniques that use associated learning algorithms to analyse data and recognise patterns.

Each classifier approaches data in a different way, therefore for organisations to get the results they need, they need to choose the right classifiers and models. Find out more about Machine Learning algorithms

What is an example of predictive modeling in real life?

3. Assessing Risk – In the financial services industry, artificial intelligence plays a major role in building predictive models that look for risk in customers and transactions. For example, banks are using credit scoring models to evaluate the ability of a potential client to repay their loans.

4 Definitive Discrete Event Simulation Examples
4 Types of Simulation Models to Leverage in Your Business
4 Agent-Based Modeling Examples

What are the two main predictive models?

Common Predictive Algorithms – Predictive algorithms use one of two things: machine learning or deep learning. Both are subsets of artificial intelligence (AI). Machine learning (ML) involves structured data, such as spreadsheet or machine data. Deep learning (DL) deals with unstructured data such as video, audio, text, social media posts and images—essentially the stuff that humans communicate with that are not numbers or metric reads.

Random Forest: This algorithm is derived from a combination of decision trees, none of which are related, and can use both classification and regression to classify vast amounts of data. Generalized Linear Model (GLM) for Two Values: This algorithm narrows down the list of variables to find “best fit.” It can work out tipping points and change data capture and other influences, such as categorical predictors, to determine the “best fit” outcome, thereby overcoming drawbacks in other models, such as a regular linear regression. Gradient Boosted Model: This algorithm also uses several combined decision trees, but unlike Random Forest, the trees are related. It builds out one tree at a time, thus enabling the next tree to correct flaws in the previous tree. It’s often used in rankings, such as on search engine outputs. K-Means: A popular and fast algorithm, K-Means groups data points by similarities and so is often used for the clustering model. It can quickly render things like personalized retail offers to individuals within a huge group, such as a million or more customers with a similar liking of lined red wool coats. Prophet: This algorithm is used in time-series or forecast models for capacity planning, such as for inventory needs, sales quotas and resource allocations. It is highly flexible and can easily accommodate heuristics and an array of useful assumptions.

What is the main goal of predictive modeling?

What is predictive modeling? – Predictive modeling is a mathematical process used to predict future events or outcomes by analyzing patterns in a given set of input data. It is a crucial component of predictive analytics, a type of data analytics which uses current and historical data to forecast activity, behavior and trends.

Examples of predictive modeling include estimating the quality of a sales lead, the likelihood of spam or the probability someone will click a link or buy a product. These capabilities are often baked into various business applications, so it is worth understanding the mechanics of predictive modeling to troubleshoot and improve performance.

Although predictive modeling implies a focus on forecasting the future, it can also predict outcomes (e.g., the probability a transaction is fraudulent). In this case, the event has already happened (fraud committed). The goal here is to predict whether future analysis will find the transaction is fraudulent.

Predictive modeling can also forecast future requirements or facilitate what-if analysis.
Predictive modeling is a form of data mining that analyzes historical data with the goal of identifying trends or patterns and then using those insights to predict future outcomes,” explained Donncha Carroll a partner in the revenue growth practice of Axiom Consulting Partners.

“Essentially, it asks the question, ‘have I seen this before’ followed by, ‘what typically comes after this pattern.'”

What is descriptive vs predictive modelling?

Key Differences Between Predictive Analytics and Descriptive Analytics – Below is a detailed explanation of Predictive Analytics and Descriptive Analytics:

Descriptive Analytics will give you a vision of the past and tells you: what has happened? Whereas Predictive Analytics will recognize the future and tells you: What might happen in the future?
Descriptive Analytics uses Data Aggregation and Data Mining techniques to give you knowledge about the past, but Predictive Analytics uses Statistical analysis and Forecast techniques to know the future.
Descriptive Analytics is used when you need to analyze and explain different aspects of your organization, whereas Predictive Analytics is used when you need to know anything about the future and fill in the information that you do not know.
A descriptive model will exploit the past data that are stored in databases and provide you with an accurate report. A Predictive model, identifies patterns found in past and transactional data to find risks and future outcomes.
Descriptive analytics will help an organization to know where they stand in the market and present facts and figures. Whereas predictive analytics will help an organization to know how they will stand in the market in the future and forecasts the facts and figures about the company.
Reports generated by Descriptive analysis are accurate, but the reports generated by Predictive analysis are not 100% accurate it may or may not happen in the future.

What is the difference between predictive modeling and AI?

Artificial intelligence vs predictive analytics – The most glaring difference between AI and predictive analytics is that AI can be autonomous and learn on its own. On the other hand, predictive analytics often relies on human interaction to help query data, identify trends, and test assumptions, though it can also use ML in certain circumstances.

Why do we need clinical prediction models?

INTRODUCTION – Hippocrates emphasized prognosis as a principal component of medicine, Nevertheless, current medical investigation mostly focuses on etiological and therapeutic research, rather than prognostic methods such as the development of clinical prediction models.

Numerous studies have investigated whether a single variable (e.g., biomarkers or novel clinicobiochemical parameters) can predict or is associated with certain outcomes, whereas establishing clinical prediction models by incorporating multiple variables is rather complicated, as it requires a multi-step and multivariable/multifactorial approach to design and analysis,

Clinical prediction models can inform patients and their physicians or other healthcare providers of the patient’s probability of having or developing a certain disease and help them with associated decision-making (e.g., facilitating patient-doctor communication based on more objective information).

Applying a model to a real world problem can help with detection or screening in undiagnosed high-risk subjects, which improves the ability to prevent developing diseases with early interventions. Furthermore, in some instances, certain models can predict the possibility of having future disease or provide a prognosis for disease (e.g., complication or mortality).

This review will concisely describe how to establish clinical prediction models, including the principles and processes for conducting multivariable prognostic studies and developing and validating clinical prediction models.

What is the difference between predictive model and forecast?

Difference Between Predictive Analytics And Forecasting Analysis by January 27, 2023 February 17, 2023 282 What Is Predictive Modeling In Healthcare The secret is not deciding whether the model is better for your organization but rather figuring out how to exploit both models at various contextual levels of each business function if you want your company to flourish to its full potential. Do you recall the magic 8 ball? The magic 8 ball appears to “predict” or “forecast” an answer to your query at first. That doesn’t function like this in forecasting (at least, for successful companies). Instead, it can be described as a method that uses trend analysis to forecast future events based on historical and current data. Predictive modeling is a form of AI that uses the technology of and probability techniques to estimate more specific outcomes. As far as we can tell, this kind of model aids in identifying customers who are likely to buy our brand-new One AI software in the upcoming 90 days.

To do this, we can specify a desired result and proceed backwards in order to find characteristics in client data that have previously suggested that they are about to make a purchase. It would analyse the data and determine which of these elements actually helps with the sale. This modelling can evaluate the data and assist us in determining that in any case.

Our magic 8-ball is a long way from predictive modelling. At first instance, the forecasting model sounds more accurate as compared to the predictive model as it uses data from the past and the present to estimate future trends., on the other hand, is not merely guessing.

In order to anticipate all future events, complex analytics algorithms have been used, which make use of both past and present data. Predictive modeling uses methods like automated machine learning (AML) and artificial intelligence (AI) to assist you to find patterns or potential outcomes in a model.

Most people are interested in how forecasting or predictive modeling could help grow your people analytics capabilities. Do you start with or predictive modeling? The historical data is utilized as the basic model for all subsequent estimations whenever a trend in the market is predicted using the forecasting model.

For instance, using data from the pattern of the prior year, sales forecasting increases the overall sales margin for seasonal products.
Such information might help you decide how much and what kind of product to supply to the market.
In contrast, predictive analytics assists in locating potential clients inside the target market for your seasonal product.

Using these insights, you may evaluate your clients’ needs, create a correlation between demographics and customer preferences, and then use that information to inform your supply and marketing strategies. Read Our Best Fintech Article: Any successful organisation depends on having a fundamental awareness of the clientele’s inclinations and tendencies, which enables it to make choices and adjust its plans accordingly.

With forecasting, you can estimate market obstacles and opportunities and tailor your tactics to address them. This gives you the best macro-level insights into the behaviour of your clients. In short, forecasting aids in planning how to manoeuvre through the working world, making sure you avoid potential traps and risk factors, getting ready for unforeseen problems, and optimising entire processes for large gains.

It helps to understand individual preferences, rank customers effectively, and plan how to deliver a better customer experience to maximize satisfaction by providing better insight into the analytical domain. Leveraging forecasting and predictive analytics always drive better decision making.

Forecasting is a technique that takes data and predicts the future value of the data by looking at its unique trends.
For example – predicting average annual company turnover based on data from 10+ years prior.
Predictive analysis factors in a variety of inputs and predicts future behavior – not just a number.

For example – out of this same employee group, which of these employees are most likely to leave (turnover = the output), based on analyzing past employee data and identifying the indicators (input) that often proceed the output? In the first case, there is no separate input or output variable but in the second case, you use several input variables to arrive at an output variable.

What are the different techniques for predictive models?

Using statistics, probability, and data mining to predict future outcomes. – Predictive modeling is the process of taking known results and developing a model that can predict values for new occurrences. It uses historical data to predict future events.

There are many different types of predictive modeling techniques including ANOVA, linear regression (ordinary least squares), logistic regression, ridge regression, time series, decision trees, neural networks, and many more. Selecting the correct predictive modeling technique at the start of your project can save a lot of time.

Choosing the incorrect modeling technique can result in inaccurate predictions and residual plots that experience non-constant variance and/or mean. Regression analysis is used to predict a continuous target variable from one or multiple independent variables. A scatterplot for data that may be best modeled by an ANOVA model looks as so ANOVA, or analysis of variance, is to be used when the target variable is continuous and the dependent variables are categorical. The null hypothesis in this analysis is that there is no significant difference between the different groups. Linear regression is to be used when the target variable is continuous and the dependent variable(s) is continuous or a mixture of continuous and categorical, and the relationship between the independent variable and dependent variables are linear. Furthermore, all the predictor variables should be normally distributed with constant variance and should demonstrate little to no multicollinearity nor autocorrelation with one another. https://www.researchgate.net/figure/Linear-Probability-Versus-Logistic-Regression-6_fig2_224127022 Logistic regression does not require a linear relationship between the target and the dependent variable(s). The target variable is binary (assumes a value of either 0 or 1) or dichotomous.

The errors/residuals of a logistic regression need not be normally distributed and the variance of the residuals does not need to be constant. However, the dependent variables are binary, the observations must be independent of each other, there must be little to no multicollinearity nor autocorrelation in the data, and the sample size should be large.

Lastly, while this analysis does not require the independent and dependent variable(s) to be linearly related, the independent variables must be linearly related to the log odds. If the scatter plot between the independent variable(s) and the dependent variable looks like the plot above, a logistic model might be the best model to represent that data. Ridge Regression For variables that experience high multicollinearity, such as X1 and X2 in this case, a ridge regression may be the best choice in order to normalize the variance of the residuals with an error term. Ridge regression is a technique for analyzing multiple regression variables that experience multicollinearity.

Ridge regression takes the ordinary least squares approach, and honors that the residuals experience high variances by adding a degree of bias to the regression estimates to reduce the standard errors. The assumptions follow those of multiple regression, the scatter plots must be linear, there must be constant variance with no outliers, and the dependent variables must exhibit independence.

Time Series https://simplystatistics.org/2016/05/05/timeseries-biomedical/ Time-series regression analysis is a method for predicting future responses based on response history. The data for a time series should be a set of observations on the values that a variable takes at different points in time.

The data is bivariate and the independent variable is time, The series must be stationary, meaning they are normally distributed: the mean and variance of the series are constant over long periods of time. Furthermore, the residuals should also be normally distributed with a constant mean and variance over a long period of time, as well as uncorrelated.

The series should not contain any outliers. If random shocks are present, they should indeed be randomly distributed with a mean of 0 and a constant variance.

What are the limitations of predictive modeling?

1. Incompleteness – The accuracy of predictive analytics models is limited by the completeness and accuracy of the data being used. Because the analytical algorithms attempt to build models based on the available data, deficiencies in the data may lead to deficiencies in the model.

What is the alternative to predictive modelling?

Other important factors to consider when researching alternatives to Predictive Modeling include ease of use and reliability. We have compiled a list of solutions that reviewers voted as the best overall alternatives and competitors to Predictive Modeling, including Tableau, Adobe Analytics, Qlik Sense, and RapidMiner.

What is an example of predictive modeling in real life?

4 Definitive Discrete Event Simulation Examples
4 Types of Simulation Models to Leverage in Your Business
4 Agent-Based Modeling Examples

What are the two types of predictive modelling?

These models can be trained over time to respond to new data or values, delivering the results the business needs.
Predictive modelling largely overlaps with the field of machine learning,
There are two types of predictive models.
They are Classification models, that predict class membership, and Regression models that predict a number.

Decision trees: Decision trees are a simple, but powerful form of multiple variable analysis. They are produced by algorithms that identify various ways of splitting data into branch-like segments. Decision trees partition data into subsets based on categories of input variables, helping you to understand someone’s path of decisions.
Regression (linear and logistic) Regression is one of the most popular methods in statistics. Regression analysis estimates relationships among variables, finding key patterns in large and diverse data sets and how they relate to each other.
Neural networks Patterned after the operation of neuronsin the human brain, neural networks (also called artificial neural networks) are a variety of deep learning technologies. They’re typically used to solve complex pattern recognition problems – and are incredibly useful for analysing large data sets. They are great at handling nonlinear relationships in data – and work well when certain variables are unknown

Other classifiers:

Time Series Algorithms: Time series algorithms sequentially plot data and are useful for forecasting continuous values over time.
Clustering Algorithms: Clustering algorithms organise data into groups whose members are similar.
Outlier Detection Algorithms: Outlier detection algorithms focus on anomaly detection, identifying items, events or observations that do not conform to an expected pattern or standard within a data set.
Ensemble Models: Ensemble models use multiple machine learnin g algorithms to obtain better predictive performance than what could be obtained from one algorithm alone.
Factor Analysis: Factor analysis is a method used to describe variability and aims to find independent latent variables.
Naïve Bayes: The Naïve Bayes classifier allows us to predict a class/category based on a given set of features, using probability.
Support vector machines: Support vector machines are supervised machine learning techniques that use associated learning algorithms to analyse data and recognise patterns.

What is the difference between regression and predictive modeling?

That predictive modeling is about the problem of learning a mapping function from inputs to outputs called function approximation. That classification is the problem of predicting a discrete class label output for an example. That regression is the problem of predicting a continuous quantity output for an example.