## 3 Results
### Service Delivery Model for Health Monitoring and Health Management Systems
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of** & **Types of AI Used** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Descriptive \\ analytics \\ \end{tabular} } & Data visualization tools: AI-enhanced visualization tools help in presenting \\  & data in an easily understandable format, identifying patterns and trends. \\ \cline{1-1}  & Clustering algorithms: used to group similar data points, revealing patterns or \\ \cline{1-1}  & & anomalies in large datasets. \\ \hline \multirow{4}{*}{\begin{tabular}{c} Diagnostic \\ analytics \\ \end{tabular} } & Classification algorithms: AI models that categorize data into predefined \\ \cline{1-1}  & classes, useful for identifying the type or category of a problem. \\ \cline{1-1}  & Root Cause Analysis (RCA) algorithms: AI-driven RCA can automate the \\ \cline{1-1}  & identification of root causes behind observed anomalies. \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Predictive \\ analytics \\ \end{tabular} } & Machine learning models: supervised learning algorithms, such as regression \\ \cline{1-1}  & models, decision trees, and neural networks, can forecast future events based \\ \cline{1-1}  & & on historical data. \\ \cline{1-1}  & Time-series analysis: AI techniques for analyzing time-series data to predict \\ \cline{1-1}  & & future data points. \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Prescriptive \\ analytics \\ \end{tabular} } & Optimization algorithms: AI methods that find the best solution from a set of \\ \cline{1-1}  & & possible options, considering constraints and objectives. \\ \cline{1-1}  & Reinforcement learning: AI learns to make decisions by performing actions in \\ \cline{1-1}  & an environment to achieve a goal, optimizing decision-making over time. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: AI types and their applicability to analytics of HMGT. Table 4 illustrates how aviation health monitoring serves as a foundational layer, focusing on the collection and basic analysis of data to monitor the health status of aircraft. Aviation health management builds upon this, employing more sophisticated data processing, analytics, and decision-support technologies to proactively manage aircraft health, optimize maintenance, and improve operational efficiencies. Together, these models represent a comprehensive approach to maintaining and managing the health of aviation assets. HMGT provides a range of unique services designed to optimize aircraft performance, enhance safety, and reduce operational costs through proactive maintenance and management strategies. These services leverage advanced technologies, including the IoT, AI, and big data analytics, to predict potential issues before they occur and prescribe the most effective actions. Table 5 demonstrates some key services provided by health management. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Service** & \multicolumn{2}{c}{**Aviation Health**} & \multicolumn{2}{c}{**Aviation Health**} \\
**Component** & \multicolumn{2}{c}{**Monitoring**} & \multicolumn{2}{c}{**Management**} \\ \hline Decision & - & Provides data for manual review and decision-making by maintenance personnel. & - & AI and decision-support systems \\ \hline Implementation and feedback & - & Provides data for manual review and decision-making. & - & AI and decision-support systems \\ \hline Technologies used & - & IoT sensors, data loggers, and basic data analytics tools. & - & - \\ \hline Outcome & - & Improved situational awareness and early detection of potential issues. & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Key services provided by health management. By integrating these services, airlines and aircraft operators can significantly improve the reliability, safety, and efficiency of their fleets, leading to better operational outcomes and cost savings. The unique services provided by aviation HMGT rely on diverse and sophisticated sources of information to ensure accurate predictions, efficient maintenance planning, and overall operational optimization.