Back to Search
Start Over
Identification of Risk Factors Associated with Obesity and Overweight-A Machine Learning Overview
- Source :
- Sensors (Basel, Switzerland), Sensors, Sensors, Vol 20, Iss 2734, p 2734 (2020), Volume 20, Issue 9
- Publication Year :
- 2020
-
Abstract
- Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, economy, and social position slowly develop behavioral risk factors in humans. Behavioral risk factors such as unhealthy habits, improper diet, and physical inactivity lead to physiological risks, and &ldquo<br />obesity/overweight&rdquo<br />is one of the consequences. &ldquo<br />Obesity and overweight&rdquo<br />are one of the major lifestyle diseases that leads to other health conditions, such as cardiovascular diseases (CVDs), chronic obstructive pulmonary disease (COPD), cancer, diabetes type II, hypertension, and depression. It is not restricted within the age and socio-economic background of human beings. The &ldquo<br />World Health Organization&rdquo<br />(WHO) has anticipated that 30% of global death will be caused by lifestyle diseases by 2030 and it can be prevented with the appropriate identification of associated risk factors and behavioral intervention plans. Health behavior change should be given priority to avoid life-threatening damages. The primary purpose of this study is not to present a risk prediction model but to provide a review of various machine learning (ML) methods and their execution using available sample health data in a public repository related to lifestyle diseases, such as obesity, CVDs, and diabetes type II. In this study, we targeted people, both male and female, in the age group of &gt<br />20 and &lt<br />60, excluding pregnancy and genetic factors. This paper qualifies as a tutorial article on how to use different ML methods to identify potential risk factors of obesity/overweight. Although institutions such as &ldquo<br />Center for Disease Control and Prevention (CDC)&rdquo<br />and &ldquo<br />National Institute for Clinical Excellence (NICE)&rdquo<br />guidelines work to understand the cause and consequences of overweight/obesity, we aimed to utilize the potential of data science to assess the correlated risk factors of obesity/overweight after analyzing the existing datasets available in &ldquo<br />Kaggle&rdquo<br />University of California, Irvine (UCI) database&rdquo<br />and to check how the potential risk factors are changing with the change in body-energy imbalance with data-visualization techniques and regression analysis. Analyzing existing obesity/overweight related data using machine learning algorithms did not produce any brand-new risk factors, but it helped us to understand: (a) how are identified risk factors related to weight change and how do we visualize it? (b) what will be the nature of the data (potential monitorable risk factors) to be collected over time to develop our intended eCoach system for the promotion of a healthy lifestyle targeting &ldquo<br />obesity and overweight&rdquo<br />as a study case in the future? (c) why have we used the existing &ldquo<br />UCI&rdquo<br />datasets for our preliminary study? (d) which classification and regression models are performing better with a corresponding limited volume of the dataset following performance metrics?
- Subjects :
- Male
normal distribution
obesity
020205 medical informatics
Nice
02 engineering and technology
Overweight
lcsh:Chemical technology
computer.software_genre
Sklearn
Biochemistry
Analytical Chemistry
Machine Learning
0302 clinical medicine
Pregnancy
Risk Factors
0202 electrical engineering, electronic engineering, information engineering
Medicine
data visualization
lcsh:TP1-1185
030212 general & internal medicine
Instrumentation
VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550
computer.programming_language
Behavior change
Middle Aged
Atomic and Molecular Physics, and Optics
sensor data
lifestyle diseases
VDP::Medisinske Fag: 700::Helsefag: 800
classification
Female
regression
medicine.symptom
Adult
Machine learning
Article
03 medical and health sciences
Young Adult
BMI
Urbanization
Humans
overweight
Electrical and Electronic Engineering
Exercise
gradient descent
Sedentary lifestyle
business.industry
Weight change
model performance
deep learning
eCoach
medicine.disease
calibration
Obesity
hypothesis test
python
monitoring
Artificial intelligence
Prisma
business
computer
discrimination
Subjects
Details
- ISSN :
- 14248220
- Volume :
- 20
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- Sensors (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....cc4ecb084a3984debdfc60489cc22934