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首页> 外文会议>Pacific-Asia conference on knowledge discovery and data mining >A Fast Fourier Transform-Coupled Machine Learning-Based Ensemble Model for Disease Risk Prediction Using a Real-Life Dataset
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A Fast Fourier Transform-Coupled Machine Learning-Based Ensemble Model for Disease Risk Prediction Using a Real-Life Dataset

机译：基于快速傅里叶变换耦合机器学习的基于真实生活数据集的疾病风险预测集成模型

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摘要

The use of intelligent technologies in clinical decision making have started playing a vital role in improving the quality of patients' life and helping in reduce cost and workload involved in their daily healthcare. In this paper, a novel fast Fourier transform-coupled machine learning based ensemble model is adopted for advising patients concerning whether they need to take the body test today or not based on the analysis of their medical data during the past a few days. The weighted-vote based ensemble attempts to predict the patients condition one day in advance by analyzing medical measurements of patient for the past k days. A combination of three algorithms namely neural networks, support vector machine and Naive Bayes are utilized to make an ensemble framework. A time series telehealth data recorded from patients is used for experimentations, evaluation and validation. The Tunstall dataset were collected from May to October 2012, from industry collaborator Tunstall. The experimental evaluation shows that the proposed model yields satisfactory recommendation accuracy, offers a promising way for reducing the risk of incorrect recommendations and also saving the workload for patients to conduct body tests every day. The proposed method is, therefore, a promising tool for analysis of time series data and providing appropriate recommendations to patients suffering chronic diseases with improved prediction accuracy.
机译：在临床决策中使用智能技术已开始在改善患者生活质量和帮助减少日常医疗保健中的成本和工作量方面发挥至关重要的作用。在本文中，基于过去几天对他们的医学数据的分析，采用了一种新颖的基于快速傅里叶变换耦合机器学习的集成模型，为患者提供有关他们是否需要今天进行身体检查的建议。基于加权投票的合奏试图通过分析过去k天的患者医学测量结果来提前一天预测患者的病情。利用神经网络，支持向量机和朴素贝叶斯这三种算法的组合来构成整体框架。从患者记录的时间序列远程医疗数据用于实验，评估和验证。 Tunstall数据集是从2012年5月至2012年10月从行业合作伙伴Tunstall收集的。实验评估表明，所提出的模型具有令人满意的推荐准确性，为减少不正确推荐的风险以及节省患者每天进行身体检查的工作量提供了一种有希望的方法。因此，所提出的方法是用于分析时间序列数据并为患有慢性疾病的患者提供适当建议的一种有前途的工具，其预测准确性得到了提高。

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