Application Of Machine Learning In Healthcare: Analysis On MHEALTH Dataset
Application Of Machine Learning In Healthcare: Analysis On MHEALTH Dataset
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机器学习在医疗保健中的应用：MHEALTH 数据集分析
DOI：
10.21533/scjournal.v4i2.97
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Sadina Gagula
中科院分区：
文献类型：
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作者：
Muhammed Ali Kutlay;Sadina Gagula
发达国家和发展中国家的保健服务至关重要。机器学习技术在医疗保健行业的使用具有至关重要的意义，并迅速增加。医疗保健行业的公司需要利用机器学习技术来获取有价值的数据，这些数据可以在更早的阶段用于诊断疾病。在本研究中，进行了一项研究，目的是发现机器学习技术在医疗保健领域的进一步应用。研究是通过分析一个名为MHEALTH的成熟数据集进行的，该数据集包括10名不同身份的志愿者在进行12项体育活动时的身体运动和生命体征记录。使用多层感知机和支持向量机等分类算法对数据集进行分析，并对这些算法的结果进行比较，以确定最适合该数据集分析的算法。研究的目的是利用志愿者的身体运动和生命体征数据来确定不规律，然后这些发现可以用来在特定疾病发生之前诊断和避免它们。结果还可以用于监测病人或老年人的活动，并观察他们是否在做任何可能导致他们受伤或进一步疾病的禁止运动。
The healthcare services in developed and developing countries are critically important. The use of machine learning techniques in healthcare industry has a vital importance and increases rapidly. The corporations in healthcare sector need to take advantage of the machine learning techniques to obtain valuable data that could later be used to diagnose diseases at much earlier stages. In this study, a research is conducted with the purpose of discovering further use of the machine learning techniques in healthcare sector. Research was conducted by analyzing a well-established dataset called MHEALTH, comprising body motion and vital signs recordings for ten volunteers of diverse profile while performing 12 physical activities. Dataset was analyzed using certain classification algorithms such as Multilayer Perceptron and Support Vector Machine, then results from these algorithms were compared to determine the most utile algorithm for analyzing such dataset. Study aims to determine irregularities using data from body motion and vital signs of volunteers, then these findings can be used either to diagnose particular diseases before they occur and avoid them. Results can also be used to monitor movements of ill or elderly people and observe whether they are doing any prohibited movements that would lead them to injuries or further illnesses.