Tackling pandemics in smart cities using machine learning architecture

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,...

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Autores principales:	Desire Ngabo, Wang Dong, Ebuka Ibeke, Celestine Iwendi, Emmanuel Masabo
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	pandemics smart cities artificial intelligence Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/ee562cc7ffd9475b9c6faf08805639b4
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id	oai:doaj.org-article:ee562cc7ffd9475b9c6faf08805639b4
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spelling	oai:doaj.org-article:ee562cc7ffd9475b9c6faf08805639b42021-11-24T01:27:06ZTackling pandemics in smart cities using machine learning architecture10.3934/mbe.20214181551-0018https://doaj.org/article/ee562cc7ffd9475b9c6faf08805639b42021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021418?viewType=HTMLhttps://doaj.org/toc/1551-0018With 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.Desire NgaboWang Dong Ebuka Ibeke Celestine Iwendi Emmanuel MasaboAIMS Pressarticlepandemicssmart citiesartificial intelligenceBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 8444-8461 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pandemics smart cities artificial intelligence Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	pandemics smart cities artificial intelligence Biotechnology TP248.13-248.65 Mathematics QA1-939 Desire Ngabo Wang Dong Ebuka Ibeke Celestine Iwendi Emmanuel Masabo Tackling pandemics in smart cities using machine learning architecture
description	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.
format	article
author	Desire Ngabo Wang Dong Ebuka Ibeke Celestine Iwendi Emmanuel Masabo
author_facet	Desire Ngabo Wang Dong Ebuka Ibeke Celestine Iwendi Emmanuel Masabo
author_sort	Desire Ngabo
title	Tackling pandemics in smart cities using machine learning architecture
title_short	Tackling pandemics in smart cities using machine learning architecture
title_full	Tackling pandemics in smart cities using machine learning architecture
title_fullStr	Tackling pandemics in smart cities using machine learning architecture
title_full_unstemmed	Tackling pandemics in smart cities using machine learning architecture
title_sort	tackling pandemics in smart cities using machine learning architecture
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/ee562cc7ffd9475b9c6faf08805639b4
work_keys_str_mv	AT desirengabo tacklingpandemicsinsmartcitiesusingmachinelearningarchitecture AT wangdong tacklingpandemicsinsmartcitiesusingmachinelearningarchitecture AT ebukaibeke tacklingpandemicsinsmartcitiesusingmachinelearningarchitecture AT celestineiwendi tacklingpandemicsinsmartcitiesusingmachinelearningarchitecture AT emmanuelmasabo tacklingpandemicsinsmartcitiesusingmachinelearningarchitecture
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Tackling pandemics in smart cities using machine learning architecture

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