An Intelligent Diagnosis of Adenovirus Disease for Child Healthcare and Prognosis

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