An Intelligent Diagnosis of Adenovirus Disease for Child Healthcare and Prognosis

Abstract

Objective: This proposed study is based upon the recent disease Adenovirus classification of several physical activities based upon different Machine Learning algorithms. We have chosen this topic because Adenovirus mostly infected children, and we are trying to handle this virus at an early stage so that it doesn’t affect a large population like COVID-19. The social impact of this proposed study is that each and every individual can use this model free of cost to find the Adenovirus. Methods: The dataset contains 5434 physical samples with 8 body parameters. All the collected samples there are 4484 infected (Adenovirus) and 950 healthy (non-Adenovirus). Based on the collected dataset train the Machine Learning algorithms so that the Machine Learning algorithm can work as an alternative to diagnose and predict Adenovirus and non- Adenovirus accurately. Findings: For the best result of the Machine Learning classifiers, this study used Decision Tree, K-Nearest Neighbors, Support Vector Machines, Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting Classifier. The decision tree gives the best results when compared to other Machine Learning algorithms. This study demonstrates that the Decision Tree classifier performed the most effectively with an accuracy of 95% when making comparisons between Adenovirus and non-Adenovirus. Novelty: The major uniqueness of this proposed work is the recognizing of the Adenovirus from the human body so that general people can be health conscious and take precautions to prevent the Adenovirus infection.