Sains Malaysiana 50(8)(2021): 2479-2497

http://doi.org/10.17576/jsm-2021-5008-28

 

Prediction of COVID-19 Patient using Supervised Machine Learning Algorithm

(Ramalan Pesakit COVID-19 menggunakan Algoritma Pembelajaran Mesin Diselia)

 

BUVANA, M.1* & MUTHUMAYIL, K.2

 

1Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Tamil Nadu, India

 

2Department of Information Technology, PSNA College of Engineering and Technology, Tamil Nadu, India

 

Diserahkan: 7 April 2021/Diterima: 27 Jun 2021

 

ABSTRACT

One of the most symptomatic diseases is COVID-19. Early and precise physiological measurement-based prediction of breathing will minimize the risk of COVID-19 by a reasonable distance from anyone; wearing a mask, cleanliness, medication, balanced diet, and if not well stay safe at home. To evaluate the collected datasets of COVID-19 prediction, five machine learning classifiers were used: Nave Bayes, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), and Decision Tree. COVID-19 datasets from the Repository were combined and re-examined to remove incomplete entries, and a total of 2500 cases were utilized in this study. Features of fever, body pain, runny nose, difficulty in breathing, shore throat, and nasal congestion, are considered to be the most important differences between patients who have COVID-19s and those who do not. We exhibit the prediction functionality of five machine learning classifiers. A publicly available data set was used to train and assess the model. With an overall accuracy of 99.88 percent, the ensemble model is performed commendably. When compared to the existing methods and studies, the proposed model is performed better. As a result, the model presented is trustworthy and can be used to screen COVID-19 patients timely, efficiently.

Keywords: Classifier; COVID-19; machine learning; prediction; supervised learning

 

ABSTRAK

Salah satu penyakit yang paling simptomatik ialah COVID-19. Ramalan pernafasan berdasarkan pengukuran fisiologi awal dan tepat akan meminimumkan risikoCOVID-19 dengan jarak yang munasabah daripada sesiapa sahaja; memakai topeng, kebersihan, ubat-ubatan, diet seimbang dan jika tidak sihat, tinggal di rumah. Untuk menilai kumpulan data ramalanCOVID-19 yang dikumpulkan, lima pengkelasan pembelajaran mesin digunakan: Nave Bayes, Mesin Vektor Sokongan (SVM), Regresi Logistik, Jiran K-Terdekat (KNN) dan Pohon Keputusan. Set data COVID-19 daripada Repositori digabungkan dan disemak semula untuk menghapus entri yang tidak lengkap dan sejumlah 2500 kes digunakan dalam kajian ini. Ciri demam, sakit badan, hidung berair, kesukaran bernafas, sakit tekak dan hidung tersumbat, dianggap sebagai perbezaan yang paling penting antara pesakit yang menghidapCOVID-19 dan mereka yang tidak. Kami menunjukkan fungsi ramalan lima pengelasan pembelajaran mesin. Satu set data yang tersedia untuk umum digunakan untuk melatih dan menilai model. Dengan ketepatan keseluruhan 99.88 peratus, model ensembel dilakukan dengan terpuji. Jika dibandingkan dengan kaedah dan kajian yang ada, model yang dicadangkan dilakukan dengan lebih baik. Hasilnya, model yang dipersembahkan boleh dipercayai dan dapat digunakan untuk menyaring pesakit COVID-19 tepat pada waktunya.

Kata kunci: COVID-19; pembelajaran mesin; pembelajaran yang diselia; pengelas; ramalan

 

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*Pengarang untuk surat-menyurat; email: buvana@psnacet.edu.in

 

   

 

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