## 7 Conclusions and Future Work  
The model exhibited exceptional performance in calculating risk, trust, and authorization decisions. The system effectively integrated user behavior, environmental context, and health conditions to provide adaptive and secure user authentication. However, the model's accuracy difference between training and cross-validation indicated the need for further testing and tuning on diverse data to ensure its generalizability across various scenarios. Low success ratios may also be attributed to several factors like user experience, network, and medical conditions, and to capture more complex user behaviors and environmental changes, future work will require diversifying the training data to cover a wider range of user behaviors and situations. We could use contextual factors such as ambient light, social context, and network speed to estimate the risk of a login attempt. Network quality could be used to identify patterns, proximity to known devices (like Bluetooth), daily habits, and user activity and could be used to identify a particular person when analyzed over time. Contextual elements such as ambient light and the context of device usage could also be utilized to assess the risk of a login attempt. This would also involve exploring additional features and testing performance at the device level. Additionally, it is important to keep track of the users' health status and modify authentication procedures as needed to accommodate any changes. To ensure optimal performance, we will also frequently adjust the model's parameters and validate them using fresh data. To effectively address the overfitting issue, other normalization approaches might need to be considered in addition to the cross-validation and real-world data use that [PERSON] and Elastic Net suggested in this work. Given that 80% of the participants were senior users in Sub-Saharan Africa (SSA), whose socioeconomic circumstances may differ from those of other continents, some degree of geographic and demographic generalization may be limited. This is due to potential variations in financial status, amount of technological expertise, perceived usability, and overall security awareness. Nonetheless, it is possible that the findings, independent of geography or upbringing, can be applied to other demographic groups. On health conditions, future work needs to investigate if some health conditions have more effects on authentication outcomes than others. Additionally, longitudinal studies need to be conducted in the future to monitor user behavior, and health changes over time would provide deeper insights into improving model accuracy. Regarding scalability, we believe our model can only perform very well with small datasets like the one that we used in our experiment as it has few features, but we believe that since we used the algorithm for risk calculation and not the classification tasks, we can expand it by adding more features to the risk calculation engine without significant performance costs. However, as other authors have noted [74, 75], the Naive Bayes algorithm performs best on small datasets but not datasets that require intricate feature interactions on classification tasks. Because of its computational efficiency, Naive Bayes can still perform well on simple datasets that only grow in size while the non-existence of large datasets in our specific scenario prevented us from testing its effectiveness on a sizable dataset. If the dataset becomes more complex and has more feature interactions, Random Forest or Gradient Boosting are likely to perform better predictively, though they will demand more computational power. The model can be scaled for real-world deployment, especially in a healthcare setting with thousands of users; however, given that end-user devices are mobile, attention should be kept on the computational resources needed for such scalability so that the technology cost remains low.