Tackling pandemics in smart cities using machine learning architecture

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

doi:10.3934/mbe.2021418

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 6: 8444-8461. doi: 10.3934/mbe.2021418

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Research article Special Issues

Tackling pandemics in smart cities using machine learning architecture

1.
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410000, China
2.
African Center of Excellence in the Internet of Things, University of Rwanda, Kigali 3900, Rwanda
3.
School of Creative and Cultural Business, Robert Gordon University, Aberdeen, Scotland, UK
4.
School of Creative Technologies, University of Bolton, Bolton BL3 5AB, UK
5.
Department of Mathematics and Computer Science, Coal City University Enugu, Enugu 400231, Nigeria
6.
African Center of Excellence in Data Science (ACE-DS), College of Business and Economics, University of Rwanda, Kigali 4285, Rwanda

Academic Editor:Thippa Reddy Gadekallu

Received: 06 July 2021 Accepted: 12 August 2021 Published: 27 September 2021

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.
- pandemics,
- smart cities,
- artificial intelligence
Citation: Desire Ngabo, Wang Dong, Ebuka Ibeke, Celestine Iwendi, Emmanuel Masabo. Tackling pandemics in smart cities using machine learning architecture[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 8444-8461. doi: 10.3934/mbe.2021418

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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.

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