Forecasting the Respiratory Tract Infections Development on the Basis of Machine Learning and Climatic Factors Analysis With the Use of High-Level Programming Languages

This paper examines the possibility of using machine-based learning methods to detect signs of cardiovascular disease and the development of respiratory tract infections. ECG Heartbeat Categorization Dataset was taken for the study, which contains data of electrocardiograms of different heart rhythms. This article describes how machine learning methods can classify five different types of arrhythmia. The methods used for classification are: The random forest algorithm, decision tree, and a convolutional neural network. As a result of the study, the most accurate result was demonstrated by the neural network (accuracy of 0.9347)