Classification of diabetic walking through machine learning: Survey targeting senior citizens

Y. Woo, Pizarroso Troncoso Carlos Andres, Hieyong Jeong, Choonsung Shin
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引用次数: 8

Abstract

We have an interest in diabetes, a metabolic disorder in which the levels of glucose in the blood are very high. Recently, the number of senior citizens who are detected with this disease is rapidly increasing. Moreover, diabetes does not end by lowering the levels of glucose concentration in the blood since it also causes different health complications while the disease is active, reducing the lifespan of patients. Thus, this study proposed a method to predict the possibility to find diabetes at its early stages through machine learning. The dataset for training consisted of nine features of senior citizens’ walking data measured with the shoe-type IMU sensor at three different speeds (fast, slow and preferred) of 200 human subjects in their 60s-80s. With this, we created a program which is able to predict whether a patient has diabetes or not by using Machine Learning algorithms such as Logistic Regression, Support Vector Machine and Random Forest. We also compared the accuracies obtained for each algorithm and found that both Support Vector Machine and Logistic Regression models reached an 84% of accuracy. Through the analysis results, we determined the feature importance for learning, which showed high importance for fast walking features. It was discussed that this could be related to problems with diabetic plantar ulcers when patients suffered from diabetes.
通过机器学习对糖尿病患者行走的分类:针对老年人的调查
我们对糖尿病很感兴趣,这是一种代谢紊乱,血液中的葡萄糖水平非常高。最近,被发现患有这种疾病的老年人数量正在迅速增加。此外,糖尿病并不是通过降低血液中的葡萄糖浓度来结束的,因为它在疾病活跃时也会引起各种健康并发症,缩短患者的寿命。因此,本研究提出了一种通过机器学习来预测早期发现糖尿病的可能性的方法。训练数据集由200名60 ~ 80岁的老年人在三种不同的速度(快、慢、首选)下用鞋式IMU传感器测量的老年人步行数据的9个特征组成。有了这个,我们创建了一个程序,可以通过使用逻辑回归、支持向量机和随机森林等机器学习算法来预测患者是否患有糖尿病。我们还比较了每种算法获得的准确率,发现支持向量机和逻辑回归模型都达到了84%的准确率。通过分析结果,我们确定了特征的学习重要性,其中快走特征的重要性较高。这可能与糖尿病患者发生糖尿病性足底溃疡有关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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