# Rule-Based DSL for Continuous Features and ML Models Selection in Multiple Sclerosis Research
## 3 Dsl Design  
In this section, we present a DSL that is designed to support medical professionals in describing ML datasets. As discussed in Section 2, there are numerous ML models and features available, making it challenging for medical or non-technical people to select the most appropriate ones for their research. Our proposed DSL aims to provide a semantic representation of the ML process, enabling users to efficiently describe their datasets and select suitable models and features. The design is guided by key requirements ensuring its flexibility and applicability in MS research and treatment. To balance usability for domain experts with the power required for complex ML tasks, we opted for a rule-based approach to feature and model selection. This simplifies the process for non-technical users while still providing manual and customizable options for advanced users who need more control. This dual approach ensures that our DSL can cater to both novice and experienced users, making it a versatile tool for MS research. The main function of the DSL is to facilitate the selection of ML models and features by providing three distinct ways:  
1. AutoML [25]: The DSL can automatically select the best model and features based on a set of predefined criteria, such as accuracy or complexity. This approach saves time and effort for users who may not be familiar with ML techniques.
2. Manual Selection: Users can manually select the most appropriate model and features by fine-tuning them using various parameters. This method provides more control over the selection process but requires a deeper understanding of ML concepts.
3. Customizable: Users can set all these manually, providing maximum flexibility in selecting the ideal model and features for their research.  
The DSL's design incorporates several key features to enhance its functionality and user experience. It features a hierarchical structure for clear navigation, modularity for adaptability to various research scenarios, and unique identifiers for precise element reference. The system supports task-specific models, allowing researchers to tailor their approach to particular aspects of MS research. Flexible data loading capabilities enable users to easily import data from various sources, while comprehensive evaluation metrics provide thorough model assessment. We have also included mechanisms for feature selection, visualization, and handling of logical expressions, all facilitated by an auxiliary table for attribute values. MS experts can leverage this tool without extensive knowledge of ML models or programming. Their primary focus should be on identifying crucial features and defining clear objectives, such as data classification or feature relationship discovery. By utilizing AutoML methods, these experts can generate initial results efficiently. Subsequently, ML researchers can build upon these preliminary findings, refining and enhancing the models to achieve a superior performance. Our tool facilitates a streamlined ML data pipeline improvement process, accommodating users with varying levels of ML expertise. It promotes iterative enhancements through collaborative efforts among multiple stakeholders, fostering a synergistic approach to MS research and model development.