INFECTIOUS DISEASE PREDICTION BASED ON PATIENT RECORDS USING MACHINE LEARNING ALGORITHMS - A CASE STUDY

Abstract

An environmental change plays an important role to infection outbreaks. Some maladies can modify their dissemination based on climatic factors. An environmental factor such as temperature, humidity, precipitation plays a vital role to change the spread of the aliments. For example, Malaria and dengue produces their enormous outbreaks in rainy seasons. Because the rainy seasons create a positive environment for mosquito production. Machine learning is the emerging tool to forecast the disease occurrence. In this paper, the researchers discuss three aliments COVID-19, malaria and flu outbreaks based on seasons. As well as the researcher predicts which season influences which infection based on health records. And also discuss various machine learning algorithms for predicting the seasonal wise occurrence of infections using Electronic patient health records. And also forecast the winter season is more appropriate season for spreading more disease outbreaks when compared to other seasons. This information is very useful for health analyst to prevent the people earlier.