Imputation Using Machine Learning Techniques

Dhanalakshmi, J.; Ansari, Gufran Ahmad

doi:10.1109/icccsp49186.2020.9315205

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…The LSPCF biosensor is used to detect the coronavirus [18]. The authors applied AlGaN/GaN HEMTs to detect the protein in the coronavirus [19]. The missing data has been generated in the OASIS dataset for the prediction of the Alzheimer's disease [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of COVID-19 and its Impact on Alzheimer’s Patient using Machine Learning Techniques

Sivakani¹,

Masood²

2022

IJC

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In this world, there is fast growth in technology, as technology growth is there the human also move fast based on the growth in technology. New diseases also growing fast in the world. In this paper, a semi-supervised approach has been proposed for the classification of the COVID-19 and a study has been done to analyze the impact of the covid on Alzheimer’s disease patients. Coronavirus disease is a respiratory infection disease and Alzheimer’s disease is a brain disease. From the literature, it has been analyzed that, because of the coronavirus the immunity system will be affected in humans and there is a chance to affect the brain also. Classification and clustering have been done on the coronavirus dataset and validated using a 10-fold validation process. The classifiers applied are Naïve Bayes and Random Forest; the results obtained are 99.88% and 100% accuracy. Also, the clustering has been applied and 2 clusters are generated for grouping the classes. Then a study has been done for predicting the impact of the coronavirus on Alzheimer’s patients.

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Section: Literature Reviewmentioning

confidence: 99%