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All Journal IJISCS (International Journal Of Information System and Computer Science) IJISCS (International Journal of Information System and Computer Science)
Nafis Md Zawad
American International University-Bangladesh
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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DISEASE PREDICTION FROM COVID-19 MEDICAL DATA USING DATA MINING ALGORITHM Nafis Md Zawad
IJISCS (International Journal of Information System and Computer Science) Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)
Publisher : STMIK Pringsewu Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v6i3.1312

Abstract

The study was designed to introduce a technique for disease prediction by using a data mining algorithm. Here in this paper, a significant discussion has been made on the Novel Corona Virus and the creation of a model for disease prediction. The novel Coronavirus (COVID-19) pandemic has created chaos in the world. People from both developed and developing countries are facing many death tolls due to insufficient ways to prevent COVID-19. It is observed that the environment requires a quick and effective way to control the spread of COVID-19 across the globe. The use of non-clinical methods like data mining techniques can be an effective way to combat the spreading of Covid-19. To minimize the immense pressure on the healthcare system, improved ways of patients’ detection and diagnosis of the nature of the Covid-19 pandemic need to be ensured. In this study, an epidemiological dataset, and data mining models were applied for forecasting the extent of Covid-19 patients. To construct the models, the decision tree and logistic regression were used. Besides, a random forest algorithm was applied to the dataset by using ‘Python Programming Language’. The results reveal that the model created with a ‘Random Forest Data Mining Algorithm’ is more effective in predicting the likelihood of Covid virus-infected patients with the correctness (accuracy) of up to eighty percent (80%).
DISEASE PREDICTION FROM COVID-19 MEDICAL DATA USING DATA MINING ALGORITHM Nafis Md Zawad
IJISCS (International Journal of Information System and Computer Science) Vol 6, No 3 (2022): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v6i3.1312

Abstract

The study was designed to introduce a technique for disease prediction by using a data mining algorithm. Here in this paper, a significant discussion has been made on the Novel Corona Virus and the creation of a model for disease prediction. The novel Coronavirus (COVID-19) pandemic has created chaos in the world. People from both developed and developing countries are facing many death tolls due to insufficient ways to prevent COVID-19. It is observed that the environment requires a quick and effective way to control the spread of COVID-19 across the globe. The use of non-clinical methods like data mining techniques can be an effective way to combat the spreading of Covid-19. To minimize the immense pressure on the healthcare system, improved ways of patients’ detection and diagnosis of the nature of the Covid-19 pandemic need to be ensured. In this study, an epidemiological dataset, and data mining models were applied for forecasting the extent of Covid-19 patients. To construct the models, the decision tree and logistic regression were used. Besides, a random forest algorithm was applied to the dataset by using ‘Python Programming Language’. The results reveal that the model created with a ‘Random Forest Data Mining Algorithm’ is more effective in predicting the likelihood of Covid virus-infected patients with the correctness (accuracy) of up to eighty percent (80%).
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