Sandhu Rajinder, Gill Harsuminder K, Sood Sandeep K
Department of Computer Science and Engineering, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab, India.
J Comput Sci. 2016 Jan;12:11-22. doi: 10.1016/j.jocs.2015.11.001. Epub 2015 Nov 10.
H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively.
H1N1是一种传染性病毒，传播时会影响大量人群。它是一种空气传播疾病，传播容易且死亡率高。利用云计算开发医疗支持系统正成为一种有效的解决方案，具有服务质量更好、成本降低和灵活性等优点。本文提出了一种有效的云计算架构，该架构可预测H1N1感染患者并提供预防措施以控制感染率。它由四个处理组件以及安全的云存储医疗数据库组成。随机决策树用于根据患者的症状初步评估其感染情况。社交网络分析（SNA）用于呈现疫情状态。所提出的架构在为两百万用户生成的合成数据上进行了测试。该系统在亚马逊EC2云上的分类准确率为94%，资源利用率约为81%。本文的关键点是使用SNA图来计算感染用户在传播疫情中的作用，即疫情角色指数（ORI）。这将有助于政府机构和医疗部门有效地呈现、分析和预防疫情。
