Paper Info
Title
Comparative study of existing personalized approaches for identifying important gene markers and for risk estimation in Type2 Diabetes in Italian population.
Abstract
Chronic diseases, a major health problem throughout the world, are increasing and have very high prevalence. The current project suggests a method that can be used to predict the personalized risk of chronic disease such as type 2 diabetes and inform lifestyle recommendations based on clinical, nutritional and genetic variables. The main aim is to discover new knowledge and build a personalized risk prediction model using existing methods which can be used for disease prognosis and the improvement of human lifestyle and health. Clinical and genetic data has been used to build personalized model for Italian people living in Italy. Many different methods have been used to select few genes from 87 genes. TWNFI (Transductive neuro-fuzzy inference system) developed by Prof. Nikola Kasabov and Dr Qun Song (Song and Kasabov 2006) has been used to build personalized model and has been compared with other methods. It has been found that TWNFI not only gives highest accuracy, also gives weights of variables as per their importance for risk of disease and sets of rules which can be used for better prediction and recommendation.
Year
DOI
Venue
2015
10.1007/s12530-013-9083-8
Evolving Systems
Keywords
Field
DocType
Personalized modeling, Neuro-fuzzy inference system, Type 2 diabetes, Genes related to type 2 diabetes, TWNFI
Transduction (machine learning),Population,Diabetes mellitus,Disease,Computer science,Artificial intelligence,Chronic disease,Machine learning,Inference system,Genetic marker
Journal
Volume
Issue
ISSN
6
1
1868-6486
Citations 
PageRank 
References 
0
0.34
8
Authors
4
Name
Order
Citations
PageRank
Anju Verma1192.70
Maurizio Fiasché2499.23
Maria Cuzzola3194.12
Giuseppe Irrera451.61