CN110689963A - 基于深度学习的分析食物营养成分的慢性病风险预测方法 - Google Patents
基于深度学习的分析食物营养成分的慢性病风险预测方法 Download PDFInfo
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Abstract
本发明公开了一种基于深度学***台搭建一个含有隐藏层的神经网络,将大量标签放入神经网络之中,经过训练得到营养成分对慢性病的风险预测结果。本发明能够提醒用户饮食对慢性病的直面影响,有效及时修改饮食计划,保障身心健康。
Description
技术领域
本发明涉及移动医疗信息化平台领域、机器学习等领域,特别涉及一种基于深度学习的分析食物营养成分的慢性病风险预测方法。
背景技术
机器学习是在近20多年逐渐兴起的一门多领域交叉学科,涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。机器学习理论主要是设计、分析一些让计算机可以自动进行“学习”过程的算法,也就是说从数据中自动分析获得其中隐含的规律,并利用该规律对未知数据进行分析预测或判别的算法。它是人工智能的核心,是使计算机具有智能的根本途径,其应用遍及人工智能的各个领域,并在其他领域的数据处理和分析问题上应用广泛。
进入21世纪,生命科学特别是基因科技已经广泛而且深刻影响到每个人的健康生活,于此同时,科学家们借助基因科技史无前例的用一种全新的视角解读生命和探究疾病本质。人工智能(AI)能够处理分析海量医疗健康数据,通过认知分析获取洞察,服务于政府、健康医疗机构、制药企业及患者,实现个性化,可以循证的智慧医疗,推动创新,实现价值。
发明内容
基于上述技术背景,本发明提出了一种基于深度学习的分析食物营养成分的慢性病风险预测方法,利用摄入营养成分与患病情况结合的用户样本,经过神经网络进行训练得到营养成分对慢性病的风险预测结果。
本发明的一种基于深度学习的分析食物营养成分的慢性病风险预测方法,该方法包括以下步骤:
步骤1、用户记录日常饮食的饮食摄入信息以及患病情况,对有关用户的不同患病情况分别做不同的标记;
步骤2、通过食材营养数据库、菜肴营养数据库两个数据库,将步骤1中获得的饮食摄入数据转化为分析所需要的26种营养成分数据,并且将转化得到的营养成分数据与用户的患病情况做一一对应形成标签;
步骤3、重复上述步骤,获得多组用户的有关26种营养成分数据与患病情况对应的标签;
步骤4、对得到的标签使用残差分析的方法,剔除对患病无影响或影响不大的营养成分,从而形成新的标签;
步骤5、利用Keras平台搭建一个含有隐藏层的神经网络,含有一个输入层、两个隐藏层、一个输出层的26-52-52-2神经元结构;输入为26种营养成分为26维,所以输入层设置26个神经元;2个隐藏层神经元个数分别设置为输入层的2倍为52个神经元,输出层设置为2个神经元;
将大量标签放入神经网络之中,经过多次训练,得到营养成分对慢性病的风险预测结果。
与现有技术相比,本发明具有以下优点:
本发明能够提醒用户饮食对慢性病的直面影响,有效及时修改饮食计划,保障身心健康。
附图说明
图1为本发明的一种基于深度学习的分析食物营养成分的慢性病风险预测方法的流程图;
图2为深度神经网络结构示意图。
具体实施方式
下面结合附图和实施例对本发明技术方案进行详细描述。
本发明的整体思路是采用基于互联网智能手机终端结合深度学习技术,实现食物营养成分对慢性病的风险预测,符合营养科学的科学规律。
如图1所示,为本发明的一种基于深度学习的分析食物营养成分的慢性病风险预测方法流程图,该流程包括以下步骤:
步骤1、用户通过智能手机记录日常饮食的饮食摄入信息以及自己的患病情况,饮食摄入数据包括食材或者菜肴的名称以及具体用量,有关用户的不同患病情况以不同的数字做标记;
步骤2、通过食材营养数据库、菜肴营养数据库两个数据库,将步骤1中获得的饮食摄入数据转化为分析所需要的26种营养成分数据,并且将转化得到的营养成分数据与用户的患病情况做一一对应形成标签;
步骤3、重复上述步骤,获得大量用户的(至少1000个)有关26种营养成分数据与患病情况对应的标签;
步骤4、对得到的标签使用残差分析的方法,剔除对患病无影响或影响不大的营养成分,从而形成新的标签;
步骤5、利用Keras平台搭建一个深度神经网络(DNN),含有一个输入层、两个隐藏层、一个输出层的26-52-52-2神经元结构;输入为26种营养成分为26维,所以输入层设置26个神经元;2个隐藏层神经元个数分别设置为输入层的2倍为52个神经元,输出层设置为2个神经元,深度神经网络结构如图2所示。随后将大量标签放入神经网络之中,经过多次训练,得到每个人的营养成分对慢性病的风险预测结果。
Claims (1)
1.一种基于深度学习的分析食物营养成分的慢性病风险预测方法,其特征在于:该方法包括以下步骤:
步骤1、用户记录日常饮食的饮食摄入信息以及患病情况,对有关用户的不同患病情况分别做不同的标记;
步骤2、通过食材营养数据库、菜肴营养数据库两个数据库,将步骤1中获得的饮食摄入数据转化为分析所需要的26种营养成分数据,并且将转化得到的营养成分数据与用户的患病情况做一一对应形成标签;
步骤3、重复上述步骤,获得多组用户的有关26种营养成分数据与患病情况对应的标签;
步骤4、对得到的标签使用残差分析的方法,剔除对患病无影响或影响不大的营养成分,从而形成新的标签;
步骤5、利用Keras平台搭建一个含有隐藏层的神经网络,含有一个输入层、两个隐藏层、一个输出层的26-52-52-2神经元结构;输入为26种营养成分为26维,所以输入层设置26个神经元;2个隐藏层神经元个数分别设置为输入层的2倍为52个神经元,输出层设置为2个神经元;
将大量标签放入神经网络之中,经过多次训练,得到营养成分对慢性病的风险预测结果。
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CN113539427A (zh) * | 2020-04-22 | 2021-10-22 | 深圳市前海高新国际医疗管理有限公司 | 基于卷积神经网络的营养干预分析***及分析方法 |
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CN113539427A (zh) * | 2020-04-22 | 2021-10-22 | 深圳市前海高新国际医疗管理有限公司 | 基于卷积神经网络的营养干预分析***及分析方法 |
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