Open AccessPlasmodium Detection using Machine Learning
Author(s) -
K. S. Neetha,
Rico-Méndez Favio G,
Sai Sharvesh R,
M. R.,
K Pramodh
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c6002.049420
Subject(s) - malaria , christian ministry , public health , artificial intelligence , health care , computer science , transmission (telecommunications) , machine learning , environmental health , medicine , political science , nursing , immunology , telecommunications , law
Plasmodium is one of India's biggest public health problems. Early prediction of a malaria epidemic is that the secret to malaria morbidity management, mortality as well as reducing the risk of malaria transmission in the community will benefit politicians, health care providers, medical officers, health ministry and other health organizations to better target medical resources to areas of greatest need. In this project, we acquire data sets from hospital databases, which have the information about the causes of malaria, and the images of cells infected with malaria. We then analyze these data sets and feed them to our machine-learning model. Here we are using contour detection and random forest algorithms for training the model and predicting the output