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

Abstract

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.

Predicting Life Style of Early Diabetes Mellitus using Machine Learning Technique

“…According to [23] in the world there are 232 million people who even do not know that they have diabetes mainly due to ignorance and an underfunded healthcare system. The PIMA diabetic prediction dataset was utilized by [24] to test a variety of MLT. Four advanced MLTs were used: Support Vector Classification, Logistic Regression, K nearest Neighbor, Random Forest and an accuracy of 92.85 was achieved by RF SVC (91.5%), LR(83.11) and KNN (89.6).…”