A Proposed Model for Lifestyle Disease Prediction Using Support Vector Machine
A Proposed Model for Lifestyle Disease Prediction Using Support Vector Machine
复制标题
使用支持向量机预测生活方式疾病的拟议模型
DOI：
10.1109/icccnt.2018.8493897
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Rupesh Mishra
中科院分区：
文献类型：
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作者：
Mrunmayi Patil;Vivian Brian Lobo;Pranav Puranik;Aditi Pawaskar;A. Pai;Rupesh Mishra
与一个人或一群人的生活方式有关的疾病被称为生活方式疾病。医疗保健行业收集了大量与疾病相关的数据，不幸的是，这些数据没有被挖掘出来以发现可用于有效决策的隐藏信息。本研究旨在了解支持向量机，并使用它来预测个人可能易患的生活方式疾病。此外，我们提出并模拟了一种经济机器学习模型，作为脱氧核糖核酸测试的替代方案，该模型分析个人的生活方式，以确定可能的威胁，这些威胁构成了诊断测试和疾病预防的基础，这些威胁可能是由于不健康的饮食和过量的能量摄入，身体休眠，该模拟模型将被证明是一种智能的低成本替代方案，以检测由不健康的生活方式引起的可能的遗传疾病。
Diseases that are associated with the way a person or group of people live are known as lifestyle diseases. Healthcare industry collects enormous disease-related data that is unfortunately not mined to discover hidden information that could be used for effective decision making. This study aims to understand support vector machine and use it to predict lifestyle diseases that an individual might be susceptible to. Moreover, we propose and simulate an economic machine learning model as an alternative to deoxyribonucleic acid testing that analyzes an individual's lifestyle to identify possible threats that form the foundation of diagnostic tests and disease prevention, which may arise due to unhealthy diets and excessive energy intake, physical dormancy, etc. The simulated model will prove to be an intelligent low-cost alternative to detect possible genetic disorders caused by unhealthy lifestyles.