Tackling pandemics in smart cities using machine learning architecture.

Ngabo, Desire; Dong, Wang; Ibeke, Ebuka; Iwendi, Celestine; Masabo, Emmanuel

Abstract

With the recent advancement in analytic techniques and the increasing generation of healthcare data, artificial intelligence (AI) is reinventing the healthcare system for tackling pandemics securely in smart cities. AI tools continue register numerous successes in major disease areas such as cancer, neurology and now in new coronavirus SARS-CoV-2 (COVID-19) detection. COVID-19 patients often experience several symptoms which include breathlessness, fever, cough, nausea, sore throat, blocked nose, runny nose, headache, muscle aches, and joint pains. This paper proposes an artificial intelligence (AI) algorithm that predicts the rate of likely survivals of COVID-19 suspected patients based on good immune system, exercises and age quantiles securely. Four algorithms (Naïve Bayes, Logistic Regression, Decision Tree and k-Nearest Neighbours (kNN)) were compared. We performed True Positive (TP) rate and False Positive (FP) rate analysis on both positive and negative COVID patients data. The experimental results show that kNN, and Decision Tree both obtained a score of 99.30% while Naïve Bayes and Logistic Regression obtained 91.70% and 99.20%, respectively on TP rate for negative patients. For positive COVID patients, Naïve Bayes outperformed other models with a score of 10.90%. On the other hand, Naïve 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.

Citation

NGABO, D., DONG, W., IBEKE, E., IWENDI, C. and MASABO, E. 2021. Tackling pandemics in smart cities using machine learning architecture. Mathematical biosciences and engineering [online], 18(6): the advances in cybersecurity theory and applications, pages 8444-8461. Available from: https://doi.org/10.3934/mbe.2021418

• Journal Article Type: Article
• Acceptance Date: Aug 13, 2021
• Online Publication Date: Sep 27, 2021
• Publication Date: Dec 31, 2021
• Deposit Date: Aug 13, 2021
• Publicly Available Date: Aug 13, 2021
• Journal: Mathematical biosciences and engineering
• Print ISSN: 1547-1063
• Electronic ISSN: 1551-0018
• Publisher: American Institute of Mathematical Sciences
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 6
• Pages: 8444-8461
• DOI: https://doi.org/10.3934/mbe.2021418
• Keywords: Pandemics; Smart cities; Artificial intelligence; COVID-19
• Public URL: https://rgu-repository.worktribe.com/output/1406197