Development of cardiovascular disease and diabetes risk assessment model for ethnically diverse populations

• Grant number: 16/50022-9
• Support Opportunities: Regular Research Grants
• Start date: August 01, 2016
• End date: July 31, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Agreement: Australian Technology Network of Universities (ATN)
• Mobility Program: SPRINT - Projetos de pesquisa - Mobilidade
• Principal Investigator: João Paulo Papa
• Grantee: João Paulo Papa
• Principal researcher abroad: Dinesh Kant Kumar
• Institution abroad: RMIT University, Melbourne, Australia
• Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
• Associated research grant: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms, AP.R

Abstract

The project will develop intelligent classification techniques that are suitable for analysis of health factors population data. The project will perform analysis of the population data, and build partnerships with clinics, and rank the health factors associated as the cause of disease such as cardio vasculature, and diabetes. It will combine the expertise of data classification and biomedical engineering for solving a major problem of Australia and Brazil. The outcome of this research will be an improved method for risk assessment of people for cardio vascular disease and diabetes, giving timely warning and thus preventing episodes of disease. It will lead to reduced hospitalisation and better health of the populations. The project will lead to the development of a new paradigm for analysis of incomplete health parameter data. Another outcome of this work will be long term partnerships of the two groups, and the development of the multi-disciplinary teams. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

KHOJASTEH, PARHAM; PASSOS JUNIOR, LEANDRO APARECIDO; CARVALHO, TIAGO; REZENDE, EDMAR; ALIAHMAD, BEHZAD; PAPA, JOAO PAULO; KUMAR, DINESH KANT. Exudate detection in fundus images using deeply-learnable features. COMPUTERS IN BIOLOGY AND MEDICINE, v. 104, p. 62-69, JAN 2019. (16/50022-9, 13/07375-0, 14/12236-1, 16/19403-6)