Open AccessDementia Prognostication using Machine Learning
Author(s) -
Manoj Pandey,
M Kishor,
M. Ravi Shankar
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f3673.049620
Subject(s) - dementia , machine learning , prognostics , artificial intelligence , computer science , support vector machine , disease , margin (machine learning) , naive bayes classifier , artificial neural network , medicine , data mining , pathology
Since the introduction of Machine Learning in the field of disease analysis and diagnosis, it has been revolutionized the industry by a big margin. And as a result, many frameworks for disease prognostics have been developed. This paper focuses on the analysis of three different machine learning algorithms – Neural network, Naïve bayes and SVM on dementia. While the paper focuses more on comparison of the three algorithms, we also try to find out about the important features and causes related to dementia prognostication. Dementia is a severe neurological disease which renders a person unable to use memory and logic if not treated at the early stage so a correct implementation of fast machine learning algorithm may increase the chances of successful treatment. Analysis of the three algorithms will provide algorithm pathway to do further research and create a more complex system for disease prognostication.