Detection of Infectious Disease using Non-Invasive Logistic Regression Technique

The detection of infectious disease like Malaria among humans has been a challenging task for a long time. Although it is not life-threatening, if timely care is not provided to the individual, it may lead to serious health consequences. It has been realized that certain individualistic parameters like blood sugar, heart rate and body temperature are indicators of occurrence of malaria in a person. The objective of the work is to develop a logistic regression model for prediction of malaria incidence in a person based on the individual parameters. These individualistic parameters are measured non-invasively and fed to the developed logistic regression model. The proposed method detects infectious diseases in a given individual with maximum accuracy, speed and is highly reliable and robust in disease detection.