Improved accuracy for predicting the likelihood of Covid-19 using decision tree over K nearest neighbour

Improved accuracy for predicting the likelihood of Covid-19 using decision tree over K nearest neighbour

Aim: To improve the accuracy for predicting the likelihood of covid-19 using Decision Tree over K Nearest Neighbour. Materials and Methods: Decision Tree and K Nearest Neighbour with sample size (N=1810) is executed with varying training and testing splits for predicting the accuracy for Covid-19 pr...

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Hauptverfasser: Vishnuu, C. V., Divya, G
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Algorithms
Decision trees
Predictions
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Divya, G
description Aim: To improve the accuracy for predicting the likelihood of covid-19 using Decision Tree over K Nearest Neighbour. Materials and Methods: Decision Tree and K Nearest Neighbour with sample size (N=1810) is executed with varying training and testing splits for predicting the accuracy for Covid-19 prediction. The performance of the classifiers are evaluated based on their accuracy rate using covid-19 symptom dataset. Results and Discussion: The accuracy of predicting Covid-19 in Novel Decision Tree (99%) and K Nearest Neighbour (95%) is obtained. There was a statistical significance between Decision Tree and K Nearest Neighbour(p=0.000). Conclusion: Prediction of Covid-19 using the Novel Decision Tree(DT) algorithm appears to be significantly better than the K Nearest Neighbour(KNN)with improved accuracy.
doi_str_mv 10.1063/5.0188484
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V. ; Divya, G</creator><contributor>Hamzah, Norhidayah ; Hoong, Kok Sim ; Wei, Goh Wei ; Muniandy, Nagentrau ; Angeline, Lorita</contributor><creatorcontrib>Vishnuu, C. V. ; Divya, G ; Hamzah, Norhidayah ; Hoong, Kok Sim ; Wei, Goh Wei ; Muniandy, Nagentrau ; Angeline, Lorita</creatorcontrib><description>Aim: To improve the accuracy for predicting the likelihood of covid-19 using Decision Tree over K Nearest Neighbour. Materials and Methods: Decision Tree and K Nearest Neighbour with sample size (N=1810) is executed with varying training and testing splits for predicting the accuracy for Covid-19 prediction. The performance of the classifiers are evaluated based on their accuracy rate using covid-19 symptom dataset. Results and Discussion: The accuracy of predicting Covid-19 in Novel Decision Tree (99%) and K Nearest Neighbour (95%) is obtained. There was a statistical significance between Decision Tree and K Nearest Neighbour(p=0.000). Conclusion: Prediction of Covid-19 using the Novel Decision Tree(DT) algorithm appears to be significantly better than the K Nearest Neighbour(KNN)with improved accuracy.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0188484</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Algorithms ; Decision trees ; Predictions</subject><ispartof>AIP Conference Proceedings, 2024, Vol.2729 (1)</ispartof><rights>AIP Publishing LLC</rights><rights>2024 AIP Publishing LLC.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubs.aip.org/acp/article-lookup/doi/10.1063/5.0188484$$EHTML$$P50$$Gscitation$$H</linktohtml><link.rule.ids>309,310,314,780,784,789,790,794,4512,23930,23931,25140,27924,27925,76384</link.rule.ids></links><search><contributor>Hamzah, Norhidayah</contributor><contributor>Hoong, Kok Sim</contributor><contributor>Wei, Goh Wei</contributor><contributor>Muniandy, Nagentrau</contributor><contributor>Angeline, Lorita</contributor><creatorcontrib>Vishnuu, C. 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subjects Algorithms
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Predictions
title Improved accuracy for predicting the likelihood of Covid-19 using decision tree over K nearest neighbour
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