## 4 Discussion
Complex models might perform excellently on training data, but may not generalize well to new data (overfitting), while simpler models may not capture all the nuances of the disease (underfitting). Another source of uncertainty is the evolution of medical knowledge and practices. As new clinical guidelines and treatments emerge, the relevance and effectiveness of existing models may diminish if they are not updated accordingly. Furthermore, the interpretability of the models also introduces uncertainties in clinical practice. If the decision-making process of the model is not transparent, clinicians might be uncertain about the reliability of the predictions, which could hinder their adoption. Lastly, patient behavior and adherence to treatment can introduce unpredictability that is difficult for models to account for. Factors such as lifestyle changes, medication adherence, and access to healthcare services can significantly sway the outcomes that models attempt to predict. All these aspects contribute to the overall uncertainty associated with predictive models and need to be considered when integrating them into clinical workflows, communicating risks to patients, and making informed decisions based on their outputs. Hence, the study affirms the value of PyCaret-based machine learning models in addressing complex clinical questions, such as diabetes prediction and readmission rates. While the results are promising, continuous collaboration between data scientists and healthcare professionals is essential in refining these models. Future research should focus on external validation, exploring the utility of these models across various patient demographics and healthcare infrastructures, ensuring their reliability and helping to bridge the gap between machine learning and patient care. The pursuit of such interdisciplinary collaborations holds the key to unlocking the full potential of machine learning in enhancing healthcare outcomes for patients with diabetes. In looking to the future of diabetes prediction models, the emphasis must be placed on enhancing clinical applicability, accommodating the intricacies of personalized healthcare and ensuring adaptability across varying demographics and healthcare infrastructures. Clinically applicable models need to be user-friendly and easily integrated into healthcare providers' existing routines, enabling prompt and informed decisions. Furthermore, ensuring that these predictive tools are relevant and effective for diverse populations and can operate within different healthcare setups, from urban hospitals to resource-limited rural clinics, is crucial for their global utility. Investing in these areas will help to refine diabetes management and contribute to overall improvements in healthcare outcomes. The pursuit of AI transparency is a key trajectory for future developments, and it holds particular significance in the healthcare sector, where the decision-making process must be clear and justifiable. The opacity of complex models, often referred to as 'black-box' systems due to their obscure internal workings, can hinder their credibility and uptake by medical professionals who require a thorough understanding of how predictions are arrived at to inform patient care. Regulatory entities are increasingly emphasizing the need for transparency in AI tools destined for clinical applications. As certainty and acceptance grow with more-intelligible models, the articulation of AI decision processes is expected to become a standard requisite. This will not only assist healthcare professionals, but will also ensure that such systems adhere to ethical standards and regulations.