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Development of an Intelligent Mobile Health Monitoring System for the Health Surveillance System in Indonesia
IRBM ( IF 5.6 ) Pub Date : 2020-10-09 , DOI: 10.1016/j.irbm.2020.10.001
Y.A. Djawad , S. Suhaeb , Ridwansyah , H. Jaya , Fathahillah , Saharuddin

Mobile phone applications have been widely used in various fields, including health care. Generally, this technology is used to overcome problems in health care by utilising mobile phone features for facilitating basic needs in health services. This study proposes an intelligent mobile health monitoring system that can be used in rural and remote areas where health services are still lacking. The system was made based on client/server architecture. Nine symptoms of typhoid, cough and diarrhoea from 30 patients were gathered from a hospital. Based on this data, a machine learning model using Support Vector Machine (SVM) was performed to distinguish these diseases. To find the best model parameters of the SVM, three different kernels (linear, polynomial, and Radial Basis Function (RBF)) were analysed. The result showed that RBF with degree 2 provided the best result in this particular application. The system was designed to receive input from patients about symptoms of the disease they have. The mobile phone application sends the data of the symptoms using Short Message Service (SMS) to the server. Furthermore, a machine algorithm module in the server identifies to which disease it belongs to based on the machine learning model created before. The prediction result is accessible to the doctor and the nearest Community Health Center (CHC). Based on the result, the doctor proposes a treatment plan for the patient to be recorded and sent to the patient by CHC. The proposed mobile health monitoring system has run properly and is ready to be evaluated in a real situation.



中文翻译:

印度尼西亚健康监测系统智能移动健康监测系统的开发

手机应用已广泛应用于包括医疗保健在内的各个领域。通常,此技术用于通过利用手机功能来满足医疗服务的基本需求来克服医疗保健中的问题。这项研究提出了一种智能移动健康监测系统,可以在仍然缺乏医疗服务的农村和偏远地区使用。该系统是基于客户端/服务器体系结构制作的。从医院收集了30例患者的9种伤寒,咳嗽和腹泻症状。基于此数据,执行了使用支持向量机(SVM)的机器学习模型来区分这些疾病。为了找到支持向量机的最佳模型参数,分析了三个不同的内核(线性,多项式和径向基函数(RBF))。结果表明,在该特定应用中,等级2的RBF提供了最好的结果。该系统旨在接收患者输入的有关其疾病症状的信息。手机应用程序使用短消息服务(SMS)将症状数据发送到服务器。此外,服务器中的机器算法模块会根据之前创建的机器学习模型来确定其属于哪种疾病。医生和最近的社区健康中心(CHC)都可以获取预测结果。根据结果​​,医生为患者提出治疗计划,以记录并通过CHC发送给患者。拟议中的移动健康监控系统已正常运行,可以在实际情况下进行评估。该系统旨在接收患者输入的有关其疾病症状的信息。手机应用程序使用短消息服务(SMS)将症状数据发送到服务器。此外,服务器中的机器算法模块会根据之前创建的机器学习模型来确定其属于哪种疾病。医生和最近的社区健康中心(CHC)都可以获取预测结果。根据结果​​,医生为患者提出治疗计划,以记录并通过CHC发送给患者。拟议中的移动健康监控系统已正常运行,可以在实际情况下进行评估。该系统旨在接收患者有关其疾病症状的输入。手机应用程序使用短消息服务(SMS)将症状数据发送到服务器。此外,服务器中的机器算法模块会根据之前创建的机器学习模型来确定其属于哪种疾病。医生和最近的社区健康中心(CHC)都可以获取预测结果。根据结果​​,医生为患者提出治疗计划,以记录并通过CHC发送给患者。拟议中的移动健康监控系统已正常运行,可以在实际情况下进行评估。此外,服务器中的机器算法模块会根据之前创建的机器学习模型来确定其属于哪种疾病。医生和最近的社区健康中心(CHC)都可以获取预测结果。根据结果​​,医生为患者提出治疗计划,以记录并通过CHC发送给患者。拟议中的移动健康监控系统已正常运行,可以在实际情况下进行评估。此外,服务器中的机器算法模块会根据之前创建的机器学习模型来确定其属于哪种疾病。医生和最近的社区健康中心(CHC)都可以获取预测结果。根据结果​​,医生为患者提出治疗计划,以记录并通过CHC发送给患者。拟议中的移动健康监控系统已正常运行,可以在实际情况下进行评估。

更新日期:2020-10-09
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