Classification And Errors Analysis of COVID-19 Using Supervised Machine Learning Algorithm and Visualisation

During pandemic many people died as a result of the covid-19 sickness, which appeared in 2019 and spread over the world. The objective of research work is to wards the occurrence of COVID to improve classification accuracy and threshold curve predictions on real-life dataset for Receiver Operator Characteristics (ROC) value. This paper goals the real-life COVID patients from the five countries to test the experiment. The proposed methodology involves of two steps;used Weka for calculating the accuracy by applying Decision Table machine learning classifier and compare the results of all the five countries, secondly, the improvement in ROC value in terms of initial care predictions by area under ROC analysis. For our COVID dataset has 209 instances and 16 attributes, Weka has performed on the number of training instances are 184, number of Rules applied is 20, search direction has been applied in forward direction, total number of subsets evaluated is 96, merit of best subset found is 82.609 and time taken to build model is 0. 06 seconds. One advantage of our suggested mode list hat it keeps the original data intact, ensuring experiment quality. A further advantage is that the model can be used with additional data sets to produce the highest accuracy and ROC analysis out comes.Copyright © 2022, Anka Publishers. All rights reserved.

Evaluation of Cost benefit Analysis using One-R Supervised Machine Learning Algorithm for Healthcare

Pandemic was present for the entire world from 2019 to 20. Due to this reason the workload for doctors and other healthcare professionals were increased. This workload will be eased by machine learning and the development of computer-aided analytical systems. The goal of the proposed methodology is towards the prevalence of COVID-19 to cost/benefit predictions on real-life dataset. Our proposed methodology is given for weka classification for the accuracy measurement ratios by applying 1R machine learning classifiers Considering the development of clustering with positive and negative occurrences ratios in terms of cost-benefit analysis's initial care projections. In this study 1R Supervised Machine Learning Algorithm have been applied to Covid 19 dataset provided by healthcare organization. The best classification accuracy is obtained from the algorithm of 1R with 75.54%. In this paper visualization Cost/Benefit Analysis and also analysed. Copyright © 2022, Anka Publishers. All rights reserved.

Evaluation of Cost benefit Analysis using One-R Supervised Machine Learning Algorithm for Healthcare (preprint)

Pandemic was present for the entire world from 2019 to 2020. Due to this reason the workload for doctors and other healthcare professionals were increased. This workload will be eased by machine learning and the development of computer-aided analytical systems. The goal of the proposed methodology is towards the prevalence of COVID-19 to cost/benefit predictions on real-life dataset. Our proposed methodology is given for weka classification for the accuracy measurement ratios by applying 1R machine learning classifiers Considering the development of clustering with positive and negative occurrences ratios in terms of cost-benefit analysis's initial care projections. In this study 1R Supervised Machine Learning Algorithm have been applied to Covid 19 dataset provided by healthcare organization. The best classification accuracy is obtained from the algorithm of 1R with 75.54%. In this paper visualization Cost/Benefit Analysis and also analysed.