# Rule-Based DSL for Continuous Features and ML Models Selection in Multiple Sclerosis Research
## 3 Dsl Design
### Meta Model Design
However, the complexity of these research endeavors can be daunting for domain experts without basic ML knowledge. Some studies offer DSLs by simplifying the construction of ML workflows, as evidenced by research describing ML datasets [35], measuring and mitigating biases in ML models [36], and automating the ML pipeline [37]. While these methods enhance the engineering of ML workflows, there remains a utilization threshold for non-software-related domain experts to construct and filter ML models and features. Our research aims to bridge this gap by providing clear and concise syntax to facilitate training based on existing knowledge and offering an incremental process to discover optimal solutions. Figure 8: Random Forest training results for walk speed time. (**a**) Parameters comparisons in random forest for walk speed time. (**b**) Feature importance in random forest for walk speed time. Figure 4:0 Training results. (**a**) Feature importance in random forest for process speed time; (**b**) feature importance in decision tree for walk speed time. ## 6 Conclusions

This paper presents a novel DSL designed to assist medical professionals and domain experts in developing and deploying ML models for multiple sclerosis research. The DSL offers three approaches--AutoML, manual model selection, and customizable model configuration--enabling users to choose the most appropriate method and improve the results iteratively based on their expertise and requirements. The AutoML approach leverages genetic programming to automatically explore and optimize ML pipelines, reducing the need for manual intervention. The manual selection method allows experts to define specific models, features, and rules, providing greater flexibility and control. The customizable approach enables fine-tuning of model architectures and hyperparameters, catering to advanced users seeking maximum customization. The DSL was implemented and evaluated using real-world multiple sclerosis data, demonstrating its effectiveness in identifying relevant features, selecting optimal models, and predicting clinical outcomes such as process speed time and walk speed. The results highlight the importance of T2-related MRI features in predicting disease progression, underscoring the DSL's capability to uncover valuable insights from complex datasets. By integrating MLOPs practices, the DSL streamlines the end-to-end ML lifecycle, enabling efficient model deployment, monitoring, and maintenance performed by base technology like MLflow. The proposed DSL represents a significant step towards democratizing ML in the MS domain, empowering medical professionals and researchers to leverage the power of AI without extensive programming knowledge. In the future, we aim to expand the current DSL to incorporate neural network and unsupervised learning algorithms. We aim to incorporate data preprocessing and feature engineering guidance directly into the DSL, thereby improving the ability of domain experts to manage data more effectively. Moreover, the meta model of DSL can serve as a foundation to streamline the creation of more specialized DSLs tailored to various medical use cases and the specific requirements of different domains. This process of DSL construction can be automated and enhanced by AI technologies, enabling the system to be optimized incrementally. Conceptualization, W.Z., T.Z. and U.A. ; Methodology, W.Z. ; Software, W.Z. ; Validation, T.Z. ; Investigation, W.Z. and K.W. ; Resources, K.W. and T.Z. ; Writing--original draft, W.Z. ; Writing--review & editing, K.W. and T.Z. ; Visualization, W.Z. ; Supervision, K.W. and U.A. ; Project administration, K.W. ; Funding acquisition, K.W. All authors have read and agreed to the published version of the manuscript. The authors declare that financial support was received for the research, authorship, and/or publication of this article. We are grateful that our research project DigiPhenoMS is being funded by the Free State of Saxony, Germany (Funding Guideline: eHealthSax).