## 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
**ML analysis**: The NeuroPredict Platform employs ML algorithms to analyse diet-related data (nutritional intake, meal regularity, nutrient profiling, dietary preferences, or correlated caloric intake with physical activities), historical and medications or sleep-related data over an extended period, identifying patterns related to medication adherence, sleep quality, physiological responses, and social interactions. 2. **Predictive analytics**: Leveraging data from both mental health and nutritional habits, the ML model develops predictive analytics. Evaluating patterns related to nutritional intake alongside mental well-being identifies subtle changes that may predict episodes of increased symptom intensity, relapses, or alterations in dietary habits. This improves the model's ability to predict holistic changes in a person's well-being. 3. **Early warning system**: Serving as a preventative measure, the NeuroPredict Platform identifies early indicators of impending relapse or exacerbation of symptoms, including dietary modifications and mental health issues. Changes in nutritional patterns that could affect mental health can be identified through predictive analytics related to eating habits. It is possible to identify a possible change in food habits and mental health state by identifying deviations from baseline patterns, which enables thorough and prompt interventions. 4. **Personalized interventions**: The NeuroPredict Platform creates tailored interventions in response to early warnings regarding dietary modifications and mental health. In addition to addressing mental health issues, the method includes customized nutrition management techniques. These interventions could involve hints for changes in nutritional intake or physical activities in addition to suggestions for improving sleep hygiene and medication adherence. ML-driven insights on eating patterns and preferences help provide individualized dietary recommendations that support each person's unique mental health objectives. 5. **Real-time feedback**: Real-time feedback on food choices and mental health can be easily provided by the NeuroPredict Platform. Users get instantaneous advice and insights about their eating habits and mental health. The ML approach provides dynamic feedback on dietary decisions by continuously analysing dietary data. It takes into account things like nutritional balance, following dietary guidelines, and how eating habits affect mental health. Healthcare professionals receive a complete, real-time picture, which allows them to take immediate action in the areas of nutrition and mental health. 6. **Continuous learning**: The ML model is always learning and adapting, as it is continuously related to the data on eating habits and mental health. Over time, the accuracy of predictions and interventions is improved through the iterative learning process. The methodology guarantees a more sophisticated knowledge by taking changing dietary needs and preferences along with mental health issues into account. Through a continual learning approach, the system can improve synergistically, providing customized help for people who are managing their nutritional needs and schizophrenia. Therefore, the integration of advanced technologies, such as RHMS, emerges as a promising avenue for personalized and proactive care. The comprehensive monitoring facilitated by RHMS allows for a nuanced understanding of various aspects influencing schizophrenia, including medication adherence, sleep patterns, physiological responses, and social interactions. The ML analysis of the medical- and physical-related data reveals intricate patterns that can serve as early indicators of relapse or symptom exacerbation, enabling timely and personalized interventions. This can be further integrated into the  
Figure 4: The use case flow for an application of personalized approach towards the patients with schizophrenia using ML. RHMS in order to provide personalized dietary management and a good quality of life for patients with schizophrenia.