###### Abstract
The current research utilises data from the Treatment Episode Data Set Admissions (TEDS-A) for the year 2020 and utilises an AutoML interface to predict co-occurring mental health and substance use disorders among women. The rationale behind leveraging AutoML stems from its growing significance in healthcare analysis [17; 18; 19], particularly within the domain of mental health and substance use disorders [20; 21; 22], where it often leads to enhanced precision and accuracy [23]. The opportunity for AutoML arises from the need to provide a more user-friendly method for anyone to generate and implement machine learning, offering a more intuitive approach for creating and deploying models with minimal reliance on coding or complex ML infrastructure [24]. Given the limited financial resources allocated to clinical coding and the high wages of data scientists [25], it is imperative to identify a cost-effective approach that enables healthcare organisations to leverage machine learning capabilities without incurring substantial expenses. Several AutoML platforms are currently available. Certain platforms are open source whereas others are commercial. Many prominent organisations in the field of artificial intelligence, including Microsoft Azure, Google, Amazon, H2O.ai, Dataiku, and RapidMiner, have undertaken the development and dissemination of advanced systems, such as the publicly accessible Cloud AutoML [26]. Platforms such as Dataiku exemplify this shift, providing a graphical interface empowering users to fine-tune computational settings effortlessly, enhancing accessibility. Instead of being tethered to specific algorithms or coding languages, researchers gain the flexibility to explore diverse methods within a unified space, encompassing languages such as Python, R, and more, fostering the full spectrum of ML tools. Within the AutoML framework, users leverage existing algorithms and ML frameworks. The process begins with inputting data onto the platform. Users can then opt to employ a specific method or request algorithm suggestions. Once chosen, an algorithm is set up to facilitate training, seamlessly leading into the automated testing phase. This yields immediate access to ML insights, including model predictionsand performance metrics, enabling researchers to employ validated models for forecasting or analysing various phenomena. Typically, AutoML workflows initiate with basic ML algorithms known for their simplicity, user-friendliness, and rigorously evaluated models such as k-nearest neighbors and decision trees. As the analysis demands more intricate scrutiny, more complex alternatives like boosted trees or deep learning (e.g., XGBoost) come into play for analysis and evaluation. Diverse intricate models can be crafted, often formed as ensembles--a fusion of basic models leveraging the strengths of each component while mitigating individual weaknesses. The synergy within an ensemble of algorithms aims to enhance overall predictive power and model robustness. ML techniques, particularly AutoML have led to improved granularity and accuracy in various studies [11; 22; 27]. This research capitalises on the power of AutoML to automate the process of model selection, hyperparameter tuning, and feature engineering, streamlining the analytical process and enhancing the predictive accuracy of the models. the super learning (SL) model has the potential to distinguish women with co-occurring disorders from those without. The super learning algorithm is a supervised learning method that uses a loss-based approach to choose the best combination of prediction algorithms [28]. The method achieves asymptotic performance comparable to the optimal weighted combination of the basic learners, making it a highly effective strategy for addressing various issues using the same technique; it can reduce the probability of over-fitting during the training process, employing a modified version of cross-validation [29; 30].