Prediction Analytics, a specialized field within AI, harnesses the power of supervised learning, statistical techniques, machine learning, and data mining. Creating a predictive model comes with its fair share of challenges. Each stage presents unique obstacles, from understanding and preparing the data to deploying the model:

  1. Data Understanding and Preparation:

    2. Before diving into predictive modeling, gaining a thorough understanding of the data is crucial. This involves descriptive data analysis, identifying outliers, and finding correlations between different variables. Also, handling imbalanced datasets and connecting different databases is another challenging part.

  2. Model Development:

    3. Selecting a suitable predictive model is a critical decision. It requires evaluating various approaches based on performance metrics, interpretability, and complexity.
    The deployment of a predictive model requires significant time and effort. Also, deploying a predictive model is expensive and time-consuming, as it relies on using multiple tools, technologies and jumping between platforms to accomplish different tasks. Additionally, existing platforms are more technical-oriented, so you have to wait for data scientists and software engineers to help you build your scenario for many steps to accomplish your goal.

  3. Post-Modeling Challenges:

    4. Once the predictive model is developed (e.g., your model is trained from your data), there are a few critically essential steps:

    • Model Evaluation and Testing:

      • Accurate validation and testing procedures are reliable when using the prediction model during the business decision scenario. It is necessary to ensure our model’s performance in combination with other different business decision steps.

    • Deployment:

      • Deployment and scalability considerations involve transitioning the model to production, ensuring efficiency, real-time capabilities, and addressing infrastructure requirements.

    • Integrating the Predictive Model into a Decision Use Case:

      • Another critical challenge is how it enables orchestration between different types of decision models, such as combining predictive and prescriptive analysis.
      Also, Integration with existing processes can be challenging, as aligning the model's outputs with established systems may require adjustments.

Creating a predictive model is an intricate process with multiple challenges. By navigating these challenges, you can unlock the power of predictive analytics for your organization's benefit.
Let’s have a look at how we can address these challenges using FlexRule platform with a real example.

Diabetes data example

For this example, we used a dataset including 768 samples each one has eight different parameters to predict the likelihood of an individual having diabetes. These parameters include:

  1. Number of times pregnant
  2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
  3. Diastolic blood pressure (mm Hg)
  4. Triceps skin fold thickness (mm)
  5. 2-Hour serum insulin (mu U/ml)
  6. Body mass index (weight in kg/(height in m)^2)
  7. Diabetes pedigree function
  8. Age (years)

Sample-Database

By analyzing these parameters, the predictive model can assess the risk of diabetes in an individual and provide actionable insights.

Interactive Data

  • To Understand the Data:

    • To better understand the data, Booklet allows you to build interactive charts to visualize data from different angles. We used an advanced Data Viewer to analyze data, inspect and make sense of the data.
    You can easily connect to various data sources, including SQL, Excel, XML, JSON, and text. This flexibility allows you to access data from both cloud-based and on-premises sources.

  • To Prepare Data:

    • The booklet gives you an easy-to-use environment for many abilities to play with data like filtering, checking the correlations, searching, manipulating, and fixing data, supporting missing data, and many others to prepare your data to start creating a prediction model.

HeatMap
ChartsBooklet

Developing and Adjusting the best performing predictive modeling

Domain experts and operations teams can now effortlessly develop machine learning (ML) models, without the need for specialized ML expertise. The process has become simpler than ever before.
You have the ability to create, train, deploy, and execute ML models that are tailored to your specific business decision requirements. This empowers you to seamlessly incorporate ML, rules, and data into your decision-making process.
Once developed, these models can be seamlessly integrated into decision automation scenarios with just a simple click, enabling you to put them into production efficiently.
As a result, check the data and accuracy of prediction with different algorithms and automatically select the best algorithm for your data.

MLModel

Prediction Model as part of the Decision Model

  • Testing Model

    • It is advantageous to incorporate the prediction model into a decision-making process for testing in a business context, allowing for the assessment of the prediction model within a business decision scenario. After developing the business decision model, which the predictive model is a part of it, you can build multiple test cases to cover various scenarios.

  • Deployment

    • Once the ML model is built, it needs to be able to service other processes and applications. In order to address this challenge, FlexRule allows you to deploy the model as a service securely and let the other applications consume the service.

  • Using our model in decisions use case

    • You can have a combination of predictive analytics and business rules, and decision Management capability you can create a prescriptive model that provides recommendations on the upcoming predicted situation. We can use our model in different scenarios:

    Identifying Individuals at High Risk: By inputting the parameters of a person, the predictive model can determine the probability of them developing diabetes.

    Personalized Treatment Plans: Based on the predictive model's analysis, healthcare providers can create personalized treatment plans for individuals with diabetes.

    Early Intervention: Predictive analytics can help in the early detection of diabetes by flagging individuals who are likely to develop the disease in the future.

    Monitoring Disease Progression: By regularly updating the predictive model with new data, healthcare providers can monitor the progression of diabetes in individuals over time.

For example, in this model, we used the prediction model for creating a weekly meal plan.

DRD-ML

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Conclusion

By using FlexRule AutoML, professionals and domain experts could build the ML models, do experiments with different situations, deploy them as a service securely, and let the processes and applications consume the service as the single source of truth. The machine itself selects the best algorithms for the scenario with the most performing parameters and configuration to build a model in multiple iterations. It picks the best-trained machine learning model for us.
However, ML models themselves cannot deliver much business value. They are much more useful when integrated into business decision automation. And also it should be able to execute requests.

Last updated June 26th, 2023 at 02:23 pm, Published June 23rd, 2023 at 02:23 pm