Body Mass Index (BMI) is a Popular Anthropometric Tool to Measure Obesity Among Adults

Mohajan, Devajit; Mohajan, Haradhan

doi:10.56397/jimr/2023.04.06

Cited by 15 publications

(8 citation statements)

References 51 publications

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“…The optimal BMI range is 18.5 to 29.9. Adult BMI also indicates dietary status [15]. BMI classification based on the WHO scheme can be seen in table 1 [2].…”

Section: Obesitymentioning

confidence: 99%

Implementation of K-Nearest Neighbors, Naïve Bayes Classifier, Support Vector Machine and Decision Tree Algorithms for Obesity Risk Prediction

Putri,

Husna,

Cia

et al. 2024

PREDATECS

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An abnormal or excessive build-up of fat that can negatively impact one's health as a result of an imbalance in energy between calories consumed and burnt is known as obesity. The majority of ailments, such as diabetes, heart disease, cancer, osteoarthritis, chronic renal disease, stroke, hypertension, and other fatal conditions, are linked to obesity. Information technology has therefore been the subject of several studies aimed at diagnosing and treating obesity. Because there is a wealth of information on obesity, data mining techniques such as the K-Nearest Neighbors (K-NN) algorithm, Naïve Bayes Classifier, Support Vector Machine (SVM), and Decision Tree can be used to classify the data. The 2111 records and 17 characteristics of obesity data that were received from Kaggle will be used in this study. The four algorithms are to be compared in this study. In other words, using the dataset used in this study, the Decision Tree algorithm's accuracy outperforms that of the other three algorithms K-NN, Naïve Bayes, and SVM. Using the Decision Tree algorithm, the accuracy was 84.98%; the K-NN algorithm came in second with an accuracy value of 83.55%; the Naïve Bayes algorithm came in third with an accuracy rate of 77.48%; and the SVM algorithm came in last with the lowest accuracy value in this study, at 77.32%.

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“…The optimal BMI range is 18.5 to 29.9. Adult BMI also indicates dietary status [15]. BMI classification based on the WHO scheme can be seen in table 1 [2].…”

Section: Obesitymentioning

confidence: 99%

Implementation of K-Nearest Neighbors, Naïve Bayes Classifier, Support Vector Machine and Decision Tree Algorithms for Obesity Risk Prediction

Putri,

Husna,

Cia

et al. 2024

PREDATECS

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“…The SECA mBCA515 analyzer measured body weight with an accuracy of 0.1 kg. BMI was used to assess relative body weight-a popular indicator for measuring, for example, obesity-and nutritional and health status, applicable to both women and men [14]. Body composition was measured using the SECA mBCA515 analyzer (Seca GmbH & Co.…”

Section: Outcome Measurementsmentioning

confidence: 99%

Comparison of the Health Status of Vegetarians and Omnivores Based on Biochemical Blood Tests, Body Composition Analysis and Quality of Nutrition

2023

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Many vegetarians are motivated by the health aspect of starting a plant-based diet. This diet can offer many health benefits. The study aimed to check whether people on a vegetarian diet are in good health, have a good nutritional intake, and follow the principles of healthy eating compared with omnivores. Twenty-two vegetarians on a vegetarian diet for more than five years and 22 omnivores aged 18–45 were interviewed. Each of them was given a food questionnaire, body composition analysis (BIA), and biochemical blood analysis, and their 7-day diet was analyzed. Polish vegetarians exhibit similar health statuses and tend towards better health than omnivores. They have an adequate body composition. Biochemical blood analysis showed no significant differences in blood parameters between vegetarians and omnivores, despite specific deficiencies such as vitamin B12, vitamin D and elevated homocysteine levels in vegetarians. They have a better nutritional status and follow good dietary principles. However, they were more likely to consume alcohol, add salt to their meals and sweeten hot drinks. In addition, the lifestyle of vegetarians can be described as healthier, as they are more likely to engage in leisure-time physical activity and get enough sleep.

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“…According to the World Health Organization (WHO) standard, overweight was de ned as BMI ≥ 25 kg/ and obesity was de ned as BMI≥30 kg/ [20]. Euthyroid was de ned as TSH (0.55-4.78 μIU/mL), FT4 (0.89-1.76 ng/dl), and FT3 (2.3-4.2 pg/mL) within the reference ranges.…”

Section: De Nitions and Calculations Of Variablesmentioning

confidence: 99%

Impaired Sensitivity to Thyroid Hormones is Associated with High Insulin Resistance Levels in the Euthyroid Chinese Population

Cui,

Wei,

Liu

et al. 2023

Preprint

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Background: The association between thyroid hormones sensitivity and insulin resistance (IR) remains unknown. This study was designed to analyze the relationship between sensitivity to thyroid hormones and IR stratified by different levels of obesity. Methods: 32478 euthyroid participants were included and they were divided into three groups according to the body mass index (BMI): normal weight (n=20200), overweight (n=10178) and obesity (n=2100). We used The Thyroid Feedback Quantile-based Index (TFQI), The TSH Index (TSHI), the Thyrotrophic T4 Resistance Index (TT4RI) to represent thyroid hormones sensitivity and used Triglyceride-Glucose (TyG) Index, the Atherogenic Index of Plasma (AIP), The Metabolic Score for Insulin Resistance (METS-IR) to represent IR. The association between thyroid hormones sensitivity and IR were analyzed by linear regression and logistic regression. Results: Linear regression analysis revealed that TFQI, TSHI and TT4RI were positively associated with TyG, AIP and METS-IR. Logistic regression showed that in the normal weight group, the odds ratios (ORs) for having high TyG in the highest quartiles of the TFQI, TSHI, and TT4RI were 1.430 (95% CI: 1.288,1.587); 1.537 (95% CI: 1.388,1.703); 1.518 (95% CI: 1.370,1.682) respectively (all p<0.001) compared with the lowest quartiles of the TFQI, TSHI, and TT4RI; In the overweight group, the ORs for having high TyG in the highest quartiles of the TFQI, TSHI, and TT4RI were 1.269 (95% CI:1.104,1.459); 1.363 (95% CI:1.186,1.566); 1.353 (95% CI:1.177,1.555) respectively (all p< 0.001); In the obesity group, the ORs for having high TyG in the highest quartiles of the TFQI, TSHI, and TT4RI were 1.298 (95% CI:0.951,1.774) (p=0.101); 1.454 (95% CI:1.065,1.989) (p=0.05); 1.455 (95% CI:1.066,1.991) (p=0.05) respectively. AIP and METS-IR yielded roughly similar results. Conclusion: In euthyroid adults, reduced sensitivity to thyroid hormones was associated with high levels of IR. Our study provides insight for further exploring the pathogenesis of metabolic syndrome as well as guidance for its treatment.

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Body Mass Index (BMI) is a Popular Anthropometric Tool to Measure Obesity Among Adults

Cited by 15 publications

References 51 publications

Implementation of K-Nearest Neighbors, Naïve Bayes Classifier, Support Vector Machine and Decision Tree Algorithms for Obesity Risk Prediction

Implementation of K-Nearest Neighbors, Naïve Bayes Classifier, Support Vector Machine and Decision Tree Algorithms for Obesity Risk Prediction

Comparison of the Health Status of Vegetarians and Omnivores Based on Biochemical Blood Tests, Body Composition Analysis and Quality of Nutrition

Impaired Sensitivity to Thyroid Hormones is Associated with High Insulin Resistance Levels in the Euthyroid Chinese Population

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