## 3 Perspectives and Outcomes of the Presented RHMS with a Focus on Diet-Related Approaches
### Schizophrenia, Dietary Habits, and Physical Health
#### 3.2.2 RHMS Role (NeuroPredict Platform) in Managing Schizophrenia
This dynamic approach enables healthcare professionals to observe fluctuations, trends, or sudden deviations in an individual's dietary patterns as they occur, providing insights into potential correlations with mental health states;
* Predictive analytics for proactive management: Leveraging predictive analytics, the NeuroPredict Platform anticipates challenges related to neurological health and dietary management. By identifying patterns and trends in health data, healthcare professionals can proactively address potential issues before they escalate. Predictive analytics, thus, enable the system to detect deviations from baseline dietary patterns, signalling possible changes in mental health status, enhancing the management of both schizophrenia and dietary concerns;
* Patient engagement through direct access: The platform empowers patients by providing direct access to their health data. This feedback loop is informed by continuous analysis of dietary data using ML algorithms. Patients receive real-time advice and insights about their eating habits, enabling them to make informed decisions and adjustments to their dietary choices. This engagement fosters active participation in their own care, enhancing the collaborative efforts between healthcare professionals and patients in managing diet-related diseases. * Limitations
* Scope of neurological monitoring: While the platform excels in capturing certain neurological signals, there may be opportunities for expansion to encompass a broader spectrum of neurological parameters. Including additional monitoring capabilities could provide a more comprehensive neurological health assessment. Comprehensive monitoring of brain activity, including cognitive functions related to dietary choices, is a complex endeavour. The platform's current capabilities may not provide a granular understanding of how neurological factors intricately influence dietary preferences, decision-making processes, and the cognitive aspects of nutritional intake. Moreover, understanding how individuals cognitively perceive and react to dietary suggestions or information about their eating patterns may be outside the current reach of neurological monitoring, even though physiological markers and stress responses offer insightful information;
* User education and adoption: The effectiveness of the NeuroPredict Platform relies on user understanding and consistent usage of the monitoring devices. Enhancing user education and adopting user-centric design features can address potential challenges associated with user adherence. In addition, the ML algorithms driving the NeuroPredict Platform are sophisticated and designed to autonomously analyse extensive sets of data. The complexity of these algorithms may pose challenges in terms of user comprehension, who may find it difficult to completely understand the subtleties of how ML algorithms work in the context of managing eating habits, especially when it comes to schizophrenia patients;
* Dependency on predictive models: Like any system employing predictive analytics, the accuracy of predictions depends on the continuous refinement and validation of the underlying models. Inaccuracies in predictive models may impact the platform's ability to foresee and address neurological health challenges effectively. It could be also difficult for models to generalize predictions to people with distinct dietary habits or cultural differences. For the platform to be effective, bias must be reduced, and generalisation capabilities must be improved to properly consider different dietary habits. It is also important to ensure the robustness of these models against external influences to maintain their reliability. The impact of external factors, such as changes in lifestyle, environmental influences, or socioeconomic conditions, on dietary habits, may present challenges for the reliance on predictive models, potentially introducing variability in individuals' nutritional choices.