首页> 外文会议>IEEE International Conference on Machine Learning and Applications >HWBI : Health and Well-Being Index. A Neural Network Based Index Which Quantifies the Cumulative Impact of Lifestyle Habits on Personal Health and Well-Being and Demonstration of Its Application in Managing the Risk of Diabetes
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HWBI : Health and Well-Being Index. A Neural Network Based Index Which Quantifies the Cumulative Impact of Lifestyle Habits on Personal Health and Well-Being and Demonstration of Its Application in Managing the Risk of Diabetes

机译:HWBI:健康与幸福指数。一种基于神经网络的指数,用于量化生活方式习惯对个人健康和幸福感的累积影响,并证明了其在管理糖尿病风险中的应用

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In this era of epidemiological transition, effective maintenance of personal lifestyle is touted as a sustainable strategy to minimize the risk of contracting lifestyle related diseases. Understanding of the cumulative impact of multiple lifestyle habits on overall health can be cognitively challenging for an individual. Therefore, to simplify this conundrum, we present a neural network based HWBI, which helps the person understand the impact of lifestyle interventions/habits upon his overall health and well-being. We argue that a neural network based approach captures the inter-relationship between the various lifestyle factors better than a multivariate regression model and outputs a holistic health and well-being index. We further validate the utility of HWBI in estimating the risk of diabetes for different age groups. Based on the results of the inter-relationship between HWBI and risk of diabetes, we assert that such an approach could be investigated for other lifestyle related diseases in which a lifestyle index can be explored to model disease risks. This provides a future prospect to quantitatively measure, monitor and manage the health of the individual based on his lifestyle and hence, of the community.
机译:在这个流行病学转变时代,有效维护个人生活方式被视为一种可持续的策略,可将与生活方式有关的疾病的风险降至最低。对于个人而言,了解多种生活方式习惯对整体健康的累积影响可能是认知上的挑战。因此,为简化这个难题,我们提出了一种基于HWBI的神经网络,它可以帮助人们了解生活方式干预/习惯对他的整体健康和幸福的影响。我们认为,基于神经网络的方法比多元回归模型更好地捕获了各种生活方式因素之间的相互关系,并输出了整体健康状况。我们进一步验证了HWBI在评估不同年龄组糖尿病风险中的实用性。基于HWBI与糖尿病风险之间相互关系的结果,我们认为可以对其他与生活方式相关的疾病进行研究,其中可以探索生活方式指数来模拟疾病风险。这为根据个人的生活方式以及社区的健康状况定量测量,监测和管理个人的健康提供了未来的前景。

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