That’s key to prevent future pandemics.

Application of Artificial Intelligence to smart city is very crucial to process the patient data for efficient treatment strategies and checking for the survival rate. In this paper, we have presented a better model than the one presented by [45] which only implemented the AdaBoost algorithm, with F1 Score of 0.86 on another COVID-19 patient dataset. We have discovered that some datasets are imbalanced and cannot be predicted. Meanwhile, 11 features were considered in this latest research with data for patients who were admitted to regular ward, semi-intensive ward, intensive care units which are: Hematocrit, Basophils, Eosinophils, Platelets, Mean platelet volume, Hemoglobin, Mean corpuscular hemoglobin concentration (MCHC), Leukocytes, Lymphocytes, Mean corpuscular hemoglobin (MCH) and Red blood Cells. Therefore, the model that we have proposed is actually based on the age quantiles of the distribution of this life table survivorship function. The analysis addressed in this research is significant for studying longevity risk and survival rate of patients. Several performance metrics were employed to evaluate the proposed solution, including checking for the precision, recall, F1- Measure, False Alarm Rate (FAR), Detection Rate (DR) and Accuracy. While the above stipulated performance metrics were based on True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). On the dataset usage, we have considered four algorithms to determine which is better for this predication. The experimental results show that kNN, and Decision Tree both obtained a score of 99.30% while Nave Bayes and Logistic Regression obtained 91.70% and 99.20%, respectively on TP rate for negative patients. For positive covid patients, Nave Bayes outperformed other models with a score of 10.90%. On the other hand, Nave Bayes obtained a score of 89.10% for FP rate for negative patients while Logistic Regression, kNN, and Decision Tree obtained scores of 93.90%, 93.90%, and 94.50%, respectively. Whereas, kNN, and Decision Tree both obtained a score of 0.70% outperforming Nave Bayes and Logistic Regression whose FP rate for positive patients are 8.30% and 0.80%, respectively. The future work will be implementing a deep neural smart city network with the combination of CXR scanning computer vision models to showcase the life survivorship demographic and healthcare data with different processing models. This can provide an automation of the prediction and diagnostic system to provide rapid screening and detection for COVID-19 affected and regions patients and prepare us for future pandemic outbreaks.