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eBMI People

enhanced BMI

Revolutionizing Health Assessments with Advanced ML Technology

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Introduction to eBMI

Many studies have questioned the accuracy of the Body Mass Index (BMI), a valuable tool in health assessments [1], prompting to explore alternative approaches. Due to the variety of body types, muscle distribution, bone mass, etc., only BMI measurements could lead to misclassification [2]. As a person ages, body fat mass naturally increases, and muscle mass declines, indicating that a higher BMI in older adults can be protective against disease and early death [3]. And also the risk for heart disease and diabetes increases above a certain waist measurement in men and women [4].

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Challenges with Traditional BMI Solution

BMI may not accurately reflect the health of certain racial/ethnic populations. Studies have shown that people of Asian-Pacific descent have an increased risk of chronic disease at lower BMI cut-off points[5] Nonetheless, weight control remains a key factor in the prevention of non-communicable diseases. Recent studies highlight the utility of Machine Learning (ML) in clinical settings [6]. Our new BMI tool (eBMI) evaluates the efficacy of the traditional BMI formula and investigates the potential improvements offered by modern ML classification models, incorporating additional parameters such as age and gender, apart from the traditional height and weight [7].

References:

  1. Keys, A., et al. (1972). Indices of relative weight and obesity. Journal of Chronic Diseases, 25, 329-343.
  2. Tomiyama, A. J., et al. (2016). Misclassification of cardiometabolic health when using body mass index categories in NHANES 2005-2012. International Journal of Obesity, 40, 883-886.
  3. Porter Starr, K. N., & Bales, C. W. (2015). Excessive Body Weight in Older Adults: Concerns and Recommendations. Clinical Geriatric Medicine, 31(3), 311–326.
  4. The National Heart, Lung, and Blood Institute. (n.d.). Retrieved from https://www.nhlbi.nih.gov/health/educational/lose_wt/risk.htm
  5. WHO Expert Consultation. (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet, 363(9403), 157-163.
  6. Fujihara, K., et al. (2023). Machine learning approach to predict body weight in adults. Frontiers in Public Health, 11, Article 1090146.
  7. Alvarez, J. (2024). Reinventing the Body Mass Index: A Machine Learning Approach [MedRxiv 2024.04.26.24306457].