A Comparison of Various Machine Learning Algorithms for Automated Fever Detection

Various diseases causing fever-like germinal infection, viral infection, mononucleosis, epidemic catarrh, acute inflammatory disease is a neurodegenerative illness that manifests itself in a range of motor and non-motor symptoms. Patients with various diseases frequently experience difficulties in the initial stages of the condition. Patients with other diseases have often symptoms like cough, cold, headache, and fever. Research provides machine learning algorithms for classifying various diseases utilizing data that are built on machine learning algorithm. This paper describes the diverse technique of supervised machine learning classification that determines the best magnificence algorithm based on statistics set, quantity of cases, and variability (features). To implement the algorithms, Fever data set was used for the classification with no of instances with attributes as independent variable and one as dependent variable for the analysis. Therefore, ML algorithms requires precision, accuracy and minimum error to have supervised predictive machine learning.