scholarly journals Identification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2734 ◽  
Author(s):  
Ayan Chatterjee ◽  
Martin W. Gerdes ◽  
Santiago G. Martinez
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Diabetes Type ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Type Ii ◽  
Diabetes Type Ii ◽  
Obesity And Overweight ◽  
Potential Risk Factors ◽  
Lifestyle Diseases

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 “obesity/overweight” is one of the consequences. “Obesity and overweight” 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 “World Health Organization” (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 >20 and <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 “Center for Disease Control and Prevention (CDC)” and “National Institute for Clinical Excellence (NICE)” 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 “Kaggle” and “University of California, Irvine (UCI) database”, 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 “obesity and overweight” as a study case in the future? (c) why have we used the existing “Kaggle” and “UCI” 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?

scholarly journals Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study (Preprint)

2020 ◽  
Author(s):  
Olubusola Oladeji ◽  
Chi Zhang ◽  
Tiam Moradi ◽  
Dharmesh Tarapore ◽  
Andrew C Stokes ◽  
...  
Keyword(s):  
Risk Factors ◽  
Information Seeking ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Obesity Prevalence ◽  
African Countries ◽  
Search Terms ◽  
Obesity And Overweight ◽  
Search Data ◽  
Overweight Prevalence

BACKGROUND The prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors. OBJECTIVE The objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa. METHODS We obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance. RESULTS The study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence. CONCLUSIONS Information-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys.

To determine risk factors associated with nephrolithiasis in patients suffering from Diabetes type II

2021 ◽  
Vol 12 (3) ◽  
pp. 1204-1211
Author(s):  
Hafsa Talat ◽  
Saba Ashraf ◽  
Taiba Suleman ◽  
Gull e Hina ◽  
Ali Hassan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Diabetes Type ◽  
Type Ii ◽  
Factors Associated ◽  
Diabetes Type Ii ◽  
Relationship Of ◽  
The Relationship

Objective: The aim to conduct this study is to see the patients suffering from diabetes type II having nephrolithiasis and the relationship of different risk factors that can also contribute to cause nephrolithiasis. Introduction: Nephrolithiasis is the condition in which stones are present in the kidneys. We studied nephrolithiasis in diabetes type II patients. We also examined different risk factors that are associated with nephrolithiasis. Then we evaluate the size of the stone, location, and the number of stones using ultrasonography.

scholarly journals Exploration of patterns predicting renal damage in patients with diabetes type II using a visual temporal analysis laboratory

2014 ◽  
Vol 22 (2) ◽  
pp. 275-289 ◽  
Author(s):  
Denis Klimov ◽  
Alexander Shknevsky ◽  
Yuval Shahar
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Renal Damage ◽  
Temporal Analysis ◽  
Diabetes Type ◽  
Type Ii ◽  
Diabetes Type Ii ◽  
Clinical Databases ◽  
Patients With Diabetes ◽  
Analysis Laboratory

Abstract Objective To analyze the longitudinal data of multiple patients and to discover new temporal knowledge, we designed and developed the Visual Temporal Analysis Laboratory (ViTA-Lab). In this study, we demonstrate several of the capabilities of the ViTA-Lab framework through the exploration of renal-damage risk factors in patients with diabetes type II. Materials and methods The ViTA-Lab framework combines data-driven temporal data mining techniques, with interactive, query-driven, visual analytical capabilities, to support, in an integrated fashion, an iterative investigation of time-oriented clinical data and of patterns discovered in them. Patterns discovered through the data mining mode can be explored visually, and vice versa. Both analysis modes are supported by a rich underlying ontology of clinical concepts, their relations, and their temporal properties. The knowledge enables us to apply a temporal-abstraction pre-processing phase that abstracts in a context-sensitive manner raw time-stamped data into interval-based clinically meaningful interpretations, increasing the results’ significance. We demonstrate our approach through the exploration of risk factors associated with future renal damage (micro-albuminuria and macro-albuminuria) and their relationship to the hemoglobin A1C (HbA1C ) and creatinine level concepts, in the longitudinal records of 22 000 patients with diabetes type II followed for up to 5 years. Results The iterative ViTA-Lab analysis process was highly feasible. Higher ranges of either normal albuminuria or normal creatinine values and their combination were shown to be significantly associated with future micro-albuminuria and macro-albuminuria. The risk increased given high HbA1C levels for women in the lower range of normal albuminuria, and for men in the higher range of albuminuria. Conclusions The ViTA-Lab framework can potentially serve as a virtual laboratory for investigations of large masses of longitudinal clinical databases, for discovery of new knowledge through interactive exploration, clustering, classification, and prediction.

scholarly journals A study to assess the prevalence of behavioral risk factors for lifestyle diseases among late adolescents in Chennai, Tamil Nadu

2017 ◽  
Vol 4 (4) ◽  
pp. 1260
Author(s):  
Evangeline Mary A. ◽  
Seenivasan P. ◽  
Shibiyeswanth R. I. ◽  
Prakash V. ◽  
Solaimuthurajagopal S. ◽  
...  
Keyword(s):  
Risk Factors ◽  
Tamil Nadu ◽  
Transitional Period ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Unhealthy Lifestyle ◽  
Lifestyle Habits ◽  
Cross Sectional ◽  
Professional Students ◽  
Lifestyle Diseases

Background: Lifestyle diseases are now the major causes of premature morbidity, mortality, and economic loss in developed and developing countries, including the younger age groups.The four major preventable behavioral risk factors are tobacco use, unhealthy diet, physical inactivity and harmful use of alcohol. Life of adolescents is a transitional period, offering them good opportunities for establishing health-promoting lifestyles. This study is done to assess the prevalence of behavioral risk factors for lifestyle diseases of college going adolescents of Chennai. Methods: This cross-sectional study was conducted among 483 randomly selected undergraduate students from randomly selected colleges in Chennai between March and September 2016 by two stage stratified sampling method using a semi-structured questionnaire. Data was fed into excel sheet and Descriptive and inferential statistical analysis was done using SPSS v.21 package. Results: The participants were between 17 and 20 years. They belonged to professional and non professional colleges. 78% students had unhealthy lifestyle habits. All the participants had at least one risk factor in them. The awareness on the risk factors was significantly less among non professional students, but they had significantly better behavioural habits than the professional students. Boys had significantly better habits than girls and students who were overweight significantly had unhealthy lifestyle habits. Conclusions: The study reflects the poor lifestyle habits of all college-aged individuals, which can be effectively improved by health education and behaviour change communication. 

scholarly journals RELATIONSHIP BETWEEN ANKLE BRACHIAL INDEX (ABI) AND CARDIOVASCULAR RISK FACTORS IN DIABETES TYPE II

2021 ◽  
Vol 53 (4) ◽  
Author(s):  
Shabnam Naveed ◽  
Zeeshan Ali ◽  
Ayesha Nageen ◽  
Syed Masroor Ahmed ◽  
Marium Fatima ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Ankle Brachial Index ◽  
Diabetes Type ◽  
Type Ii ◽  
Diabetes Type Ii

Diagnosis of diabetes type-II using hybrid machine learning based ensemble model

2018 ◽  
Vol 12 (2) ◽  
pp. 419-428 ◽  
Author(s):  
Abid Sarwar ◽  
Mehbob Ali ◽  
Jatinder Manhas ◽  
Vinod Sharma
Keyword(s):  
Machine Learning ◽  
Diabetes Type ◽  
Ensemble Model ◽  
Type Ii ◽  
Diabetes Type Ii ◽  
Hybrid Machine

scholarly journals Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study

10.2196/24348 ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. e24348
Author(s):  
Olubusola Oladeji ◽  
Chi Zhang ◽  
Tiam Moradi ◽  
Dharmesh Tarapore ◽  
Andrew C Stokes ◽  
...  
Keyword(s):  
Risk Factors ◽  
Information Seeking ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Obesity Prevalence ◽  
African Countries ◽  
Search Terms ◽  
Obesity And Overweight ◽  
Search Data ◽  
Overweight Prevalence

Background The prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors. Objective The objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa. Methods We obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance. Results The study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence. Conclusions Information-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys.

USING MACHINE LEARNING METHODS FOR THE CORRECTION OF BEHAVIORAL RISK FACTORS FOR PREVENTION OF CARDIOVASCULAR DISEASES

2020 ◽  
Vol 4 (97) ◽  
pp. 54-68
Author(s):  
GEORGII G. RAPAKOV ◽  
GENNADII T. BANSHCHIKOV ◽  
VYACHESLAV A. GORBUNOV ◽  
ALEKSEY V. UDARATIN
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Cardiovascular Diseases ◽  
Arterial Hypertension ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Confidence Estimation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Regional System

The article describes machine learning methods in the correction of behavioral risk factors while preventing cardiovascular diseases. The monitoring of health saving educational space in the regional system of medical prevention was implemented. Applying computer modeling the authors developed a model of binding rules based on the method of association rules and suggested the set of 5 logical rules for the risk factor of arterial hypertension. Decision tree method was used to induce decision rules and identify the target group to correct risk factors and increase the quality of arterial hypertension control. The present study provided the analysis and confidence estimation of the prognostic model. The results of this analysis were used to support management decisions in the regional system of preventive medicine.

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