Data Mining Techniques for Analysis of Nutrition, Physical Activity and Obesity from Behavioral Risk Factor Surveillance System Data

DATA MINING

INTRODUCTION
This project aims to analysis the real time business situation by applying
the data mining techniques. Here, we will analysis the physical activity, obesity,
nutrition from behavioral risk factor surveillance system by applying the data
mining techniques. The data mining techniques is used to provide the flexible
and effective system for sharing the evidence are in place and it is has the ability
to obtain and effectively use the best of available evidence including the
surveillance and data evaluation. It is prevent the obesity and promote the
healthy behaviors. It accesses the high quality of evidence and it has ability to
use it appropriate public health care practices including assessment,
implementation and evaluation.

RELATED WORK
This paper is describes (Sunmoo YOON, 2018), to discover the
behavior correlates of chronic disease based on nutrition, obesity and
physical activity by using the data mining approach. The purpose of this
paper is to describe the data mining methods for busing classification modes
for a chronic disease using the US behavioral risk factor surveillance system
data and to illustrate the application of the methods using a case study of
depressive disorder. The data mining methods includes the six steps of data
mining to build a disease model using the classification techniques and it
use the innovative approach to analyzing the dimensionality data.

RELATED WORK
According to this paper (S. M and J. C, 2018), describes the
behavioral risk factor surveillance system ad it is a collects the information on
a wide variety of health related behaviors. This paper summarizes the
behavioral risk factor surveillance system procedures and questionnaire
design. It provides the representativeness of the evaluation and response rate.
It uses the health care data to calculate the variable including the weights
status. It analysis the critical features of behavioral risk factor surveillance
system and to survey and weighting the methodology to analysis the data sets
by using the data mining methodologies.

DATA SET
 This project uses the Nutrition, physical activity and obesity from
behavioral risk factor surveillance system data (Datasciencecentral.com,
2018).
 The dataset includes data on adult's diet, physical activity, and weight status
from Behavioral Risk Factor Surveillance System.
 This data is used for DNPAO's Data, Trends, and Maps database, which
provides national and state specific data on obesity, nutrition, physical
activity, and breastfeeding.

DATA SET