CP5634 Data Mining: Analysis of Behavioral Risk Factors using WEKA

This project focuses on analyzing real-time business situations by applying data mining techniques to nutrition, physical activity, and obesity data from the Behavioral Risk Factor Surveillance System. It utilizes data mining techniques to establish a flexible and effective system for sharing evidence, obtaining, and effectively using available data, including surveillance and data evaluation. The project aims to prevent obesity and promote healthy behaviors by accessing and applying high-quality evidence to public health practices, including assessment, implementation, and evaluation. The analysis employs the Naive Bayes classifier within the WEKA data mining tool to analyze age, education, gender, and race/ethnicity, revealing insights into behavioral risk factors. While Naive Bayes offers efficient data analysis, it faces challenges such as incomplete training data, attribute independence, and continuous variables. Future work suggests exploring alternative algorithms like rules and trees to enhance the analysis of behavioral risk factors.

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

DATA MINING TECHNIQUES
The data mining is a process of the data and identifying the patterns and trends in
that information.
The big data uses the more extensive data mining techniques because the big data
has large size of the information and information trends. The data mining is used to
understand the how to relate, cluster, associate and map it with other data to produce
the result.
It also identify the source formats and data and then mapping the information to
provide the result can change and it discover the various elements and aspects of the
data by using the various data mining techniques .

DATA MINING TECHNIQUES
The naive bayes techniques are a classification techniques based on bayes theorem
with an assumption of independence among predictors. It assumes that the presence
of a particular features in a class.
 It is used to build and particularly useful for verge large data sets. It uses the highly
sophisticated classification methods. It provides a way of calculating the posterior
probability.
It fast and easy to predict the class of test data set and it perform the well in multi
class prediction.
The naive bayes classifier performs the better compare to other model like logistic
regression (EDUCBA, 2018).

ANALYZE AND DISCUSSION
 To analysis the nutrition, physical activity and obesity from behavioral risk
factor surveillance system data by using the weka data mining tool.
 The weka data mining tool is uses the classification method to provide the
effective data analysis for behavioral risk factor surveillance system.

DATA MINING ANALYSIS
 Open weka tool and upload the data set. It is shown below.

DATA MINING ANALYSIS
Naïve bayes Analysis for Age

DATA MINING ANALYSIS
Naïve bayes Analysis for Education