## 5 Conclusions  
As proved by recent global events, healthcare facilities are assets of extreme importance in a country and this in turn entails and obliges their smooth operation, decreased downtime and stoppages, as well as high reliability levels. Healthcare facilities in Canada are currently facing two major problems. First, the continuous pattern of deferring maintenance and renewal activities to ensure the continuation of facility operations is highly affecting the reliability and performance of hospital building systems and assets and is increasing the number of unpredictable failures. Moreover, the decreased amount of data available to study and analyze the failure patterns and condition degradation levels of hospital building systems is another major problem in hand that needs to be overcome. Accordingly, this paper proposed a hybrid deterioration prediction model that estimates the future condition of hospital building systems based on an integration between fuzzy logic, Markov chains, and genetic algorithm (GA) optimization. Fuzzy logic is employed as part of the developed model to make use of expert knowledge in modeling and estimating the deterioration expected for healthcare assets, and the transition probabilities output from the fuzzy-based model are fed into a nonhomogeneous Markov model to conclude the suitable condition rating and its underlying degradation over the course of life of a healthcare system. A sensitivity analysis was applied to the fuzzy-based methodology to check and ensure the robustness of the model against input alterations and the model was proved robust with a maximum variation of less than 8% with alterations in each input parameter separately. The probabilities were then calibrated using a GA in order to reflect the actual deterioration observable within healthcare facilities. The proposed model was applied to oxygen gas systems in hospital buildings due to their relative criticality and priority in the hospital operation, and the model was validated by means of a \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Fuzzy GA-Based Markov Model** & **Weibull Distribution** \\ \hline Mean Absolute Error & 1.405 & 9.852 \\ Root-Mean Squared Error & 1.853 & 11.429 \\ Mean Absolute Percentage Error & 5.358\% & 40.496\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the performance of the developed model against the Weibull-based model. comparison with the performance achieved by the Weibull distribution-based deterioration for a single validation dataset, and the proposed hybrid model significantly outperformed its counterpart with MAE, RMSE and MAPE values of 1.405, 1.853 and 5.358%, respectively. Consequently, the proposed model is expected to aid decision-makers in taking more informed, sound and efficient decisions regarding the maintenance, renewal, and replacements of hospital building assets. However, the developed study can be expanded to include more parameters that might impose an effect on the deterioration patterns and condition rating of hospital building components such as the maintenance, risk or mission criticality levels. This can be a point to consider for future research endeavors. Additionally, the model formulated was only compared with a Weibull-distribution due to its popularity and relative applicability for hospital building assets, but this can form a new research question which is the comparative performance of the developed model along with other previously created models in the literature. Another aspect that can be further investigated is the optimization model initiation parameters. More experimentation can be conducted on the variability in the model output as per different initial parameter values. Finally, the applicability of the developed model on asset types other than hospital buildings is an area that is worth exploring. Conceptualization, R.A., T.Z. and F.N. ; methodology, R.A.; validation, R.A., T.Z. and F.N.