# Rule-Based DSL for Continuous Features and ML Models Selection in Multiple Sclerosis Research
## 1 Introduction  
In the evolving landscape of contemporary healthcare, the emergence of ML signifies a pivotal change, reshaping diagnostic, therapeutic, and predictive approaches to enhanced precision and individualized patient care [1]. This technological shift not only streamlines medical processes but also fosters earlier detection, tailored treatments, and improved accuracy, setting a new benchmark for healthcare efficacy. In the field of MS research and treatment, ML has emerged as a powerful research method to address challenges such as diverse treatment responses and the often ambiguous early-stage symptoms. By leveraging early intervention and refining therapeutic approaches in MS, ML can improve diagnosis accuracy and enhance patient care. This is crucial in addressing issues related to misdiagnosis, and it lays the groundwork for individualized personalized medicine [2]. To enhance accuracy, it is essential to incorporate a wide range of features from various domains, including MRI metrics like T2 lesion size, count, and volume [3], as well as clinical data such as the Expanded Disability Status Scale (EDSS) [4] and functional tests such as walking tests [5]. Moreover, the complexity of MS, characterized by a multitude of features and variables, poses a significant challenge in feature selection. Additionally, the optimal configuration and selection of ML models are critical to achieving accurate and reliablepredictions; a task that is further complicated by the diversity of patient responses and the dynamic nature of the disease. Several studies have investigated the application of ML in the context of MS. For instance, one study employed support vector machines (SVMs) to forecast lesion data [6], while another utilized decision trees (DTs) to predict disease progression based on clinical data [7]. Given privacy concerns, the majority of medical ML research relies on sensitive data. Additionally, these studies employ varying features and ML models tailored to specific objectives. For domain experts, choosing the right features and ML models to meet their unique objectives proves challenging. Hence, we propose the creation of a DSL as a lightweighted access to defining custom logical rules to select pertinent features and determine the optimal ML model for training. Moreover, with the goal of assisting experts in identifying the results that best align with their expectations, it is advisable to design the system to automatically monitor outcomes and initiate actions based on predefined rules. These actions may include retraining a specific ML model using different features and parameters. To construct transparent AI in MS, it is imperative to tackle the intricate relationship between ML models, data quality, and clinical workflows. By incorporating features that emphasize explainability, interpretability, data provenance, security, and facilitate collaboration between ML experts and domain specialists, the DSL can facilitate the development of accessible AI solutions in healthcare. Furthermore, as the landscape of AI and ML continues to evolve, ongoing research and development are essential to keep DSL effective, meeting the evolving demands of domain experts and patients. The DSL offers a specialized programming framework and syntax aimed at simplifying and standardizing the creation, deployment, and management of ML models. It incorporates additional concepts, including MLOps, to enhance the process. MLOps, an abbreviation for ML operations, integrates ML technology, data engineering, and DevOps (Software development and IT operations) to automate the complete ML lifecycle, thus enabling seamless deployment, monitoring, and maintenance of ML models in production settings [8]. Adopting MLOps practices within this framework can further enhance the reliability and scalability of AI solutions in MS, ensuring that they remain robust and adaptable to new challenges and data.