A Machine Learning approach for Early Detection and Prevention of Obesity and Overweight

Nilesh P. Sable, R. Bhimanpallewar, Rajhendra H Mehta, Sara Shaikh, Anay Indani, S. Jadhav
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Abstract

More than 2.1 billion people worldwide are shuddering from overweightness or obesity, which represents approximately 30% of the world’s population. Obesity is a serious global health problem. By 2030, 41% of people will likely be overweight or obese, if the current trend continues. People who show indications of weight increase or obesity run the danger of contracting life-threatening conditions including type 2 diabetes, respiratory issues, heart disease, and stroke. Some intervention strategies, like regular exercise and a balanced diet, might be essential to preserving a healthy lifestyle. Thus, it is crucial to identify obesity as soon as feasible. We have collected data from sources like schools and colleges within our organization to create our dataset. A vast range of ages is considered and the BMI value is examined in order to determine the level of obesity. The dataset of people with normal BMI and those at risk has an inherent imbalance. The outcomes are collected and showcased via a website which also includes various preventive measures and calculators. The outcomes are promising, and clock an accuracy of about 90%.
早期发现和预防肥胖和超重的机器学习方法
全球有超过21亿人因超重或肥胖而不寒而栗,约占世界人口的30%。肥胖是一个严重的全球健康问题。如果目前的趋势继续下去,到2030年,41%的人可能会超重或肥胖。有体重增加或肥胖迹象的人有感染危及生命的疾病的危险,包括2型糖尿病、呼吸系统疾病、心脏病和中风。一些干预策略,如定期锻炼和均衡饮食,可能对保持健康的生活方式至关重要。因此,尽快确定肥胖是至关重要的。我们从组织内的学校和学院等来源收集数据来创建我们的数据集。为了确定肥胖水平,研究人员考虑了广泛的年龄范围,并检查了BMI值。BMI正常的人和有风险的人的数据集存在固有的不平衡。调查结果会在一个网站上收集和展示,该网站还包括各种预防措施和计算器。结果是有希望的,时钟的准确率约为90%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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