Pandemic coronavirus disease (Covid‐19): World effects analysis and prediction using machine‐learning techniques

Abstract

Pandemic novel Coronavirus (Covid‐19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid‐19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid‐19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures. Due to the lack of accurate Covid‐19 records and uncertainty, the standard techniques are being failed to correctly predict the epidemic global effects. To address this issue, we present an Artificial Intelligence (AI)‐based meta‐analysis to predict the trend of epidemic Covid‐19 over the world. The powerful machine learning algorithms namely Naïve Bayes, Support Vector Machine (SVM) and Linear Regression were applied on real time‐series dataset, which holds the global record of confirmed, recovered, deaths and active cases of Covid‐19 outbreak. Statistical analysis has also been conducted to present various facts regarding Covid‐19 observed symptoms, a list of Top‐20 Coronavirus affected countries and a number of coactive cases over the world. Among the three machine learning techniques investigated, Naïve Bayes produced promising results to predict Covid‐19 future trends with less Mean Absolute Error (MAE) and Mean Squared Error (MSE). The less value of MAE and MSE strongly represent the effectiveness of the Naïve Bayes regression technique. Although, the global footprint of this pandemic is still uncertain. This study demonstrates the various trends and future growth of the global pandemic for a proactive response from the citizens and governments of countries. This paper sets the initial benchmark to demonstrate the capability of machine learning for outbreak prediction.

Diyabet tanısının tahminlenmesinde denetimli makine öğrenme algoritmalarının performans karşılaştırması

“…Bazı araştırmacılar, diyabeti HbA1c, adiponektin ve BMI gibi klinik test verileriyle sınıflandırmak için makine öğrenme algoritmalarını kullansa da insan vücudunun karmaşıklığı nedeniyle, veri madenciliği teknolojisinin klinik tıpta uygulanması genel olarak hala nispeten sınırlıdır (Kalsch vd., 2015;Chen & Pan, 2018). Literatürde kalp, diyabet, karaciğer, kanser, kovid enfeksiyonu ve hepatit hastalıklarının tanısı için birçok farklı denetimli makine öğrenme algoritması kullanılmıştır (Fatima & Pasha, 2017;Özmen vd., 2018;Ali vd., 2021;Choudhury, 2021;Nindrea vd., 2021;Alabi vd., 2020;Muhammad vd., 2021;Tiwari, 2021). Denetimli makine öğrenmesi algoritmalarını hastalık tanısında kullanan 48 makalenin değerlendirildiği bir çalışmada, en sık uygulanan yöntemin SVM olduğu, onu NB yönteminin izlediği belirlenmiştir (Uddin vd., 2019).…”

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Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa

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