Data mining applications in the healthcare Sector: Addressing health promotion:
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Running Head: DATA MINING APPLICATIONS IN THE HEALTHCARE SECTOR Data mining applications in the Healthcare Sector: Addressing health promotion Name: Institutional Affiliation: Date: 1 DATA MINING APPLICATIONS IN THE HEALTHCARE SECTOR 2 Table of Contents Data mining applications in the healthcare Sector: Addressing health promotion ………………………… 3 Introduction ………………………………………………………………………………………………………………………………. 3 References …………………………………………………………………………………………………………………………………. 5 DATA MINING APPLICATIONS IN THE HEALTHCARE SECTOR 3 Data mining applications in the healthcare Sector: Addressing health promotion Introduction Over the years, numerous developments have been made concerning technology. The technology changes have allowed many people and stakeholders to boost their connection with the arising consumer needs. On the same note, it is worth appreciating the changes which continue to occur within the community when considering technological changes and their effects in promoting overall consumer living. Data mining is one of the most common technologies used today to foster analysis and make meaning out of vast data volumes (Islam, Hasan, Wang & Germack, 2018).
In the healthcare sector, diverse records are witnessed that relate to health outcomes associated with a given population. Healthcare stakeholders may need to use the right technologies and solutions to focus more on disease prevention and health promotion. Understanding the patterns in a given community, condition, or illness is essential for healthcare stakeholders because it informs their decisions. However, predicting diseases and or conditions within the healthcare sector has been a major challenge making it hard to influence preparations and resilience. With the developments witnessed today, healthcare stakeholders can create an ideal framework for handling community health issues.
Data mining is a promising technology that combines various frameworks for collecting, analyzing and visualizing information in readiness for decision making. This technology has been applied in a wide range of areas, including the healthcare sector and business environments. When applied in the healthcare sector, practitioners and stakeholders can gain insight into the best interventions to implement to promote health when responding to a given community and condition. For example, data mining can be used to extract information DATA MINING APPLICATIONS IN THE HEALTHCARE SECTOR 4 about the trends in diabetes reported in the African American population historically to influence decisions on the implemented interventions’ success rate (Mir & Dhage, 2018). this data considers multiple variables simultaneously such as incidence, prevalence, past interventions and their success rates in handling the condition.
Using this technology, it is possible to define the parameters for promoting health concerning the condition mentioned, supported by evidencebased strategies and data (Cifci & Hussain, 2018). Data mining, therefore, can be implemented in the healthcare sector as an analytics framework for promoting insight into disease incident hence promoting outcomes by informing practitioners and major stakeholders about the ideal interventions. DATA MINING APPLICATIONS IN THE HEALTHCARE SECTOR 5 References Cifci, M. A., & Hussain, S. (2018). Data mining usage and applications in health services. JOIV: International Journal on Informatics Visualization, 2(4), 225-231. Islam, M. S., Hasan, M. M., Wang, X., & Germack, H. D. (2018, June). A systematic review of healthcare analytics: application and theoretical perspective of data mining. In Healthcare (Vol. 6, No. 2, p. 54). Multidisciplinary Digital Publishing Institute. Mir, A., & Dhage, S. N. (2018, August). Diabetes disease prediction using machine learning on big data of healthcare.
In 2018 fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1-6). IEEE. Term topic replys Peer post 1 : Guthikonda Term paper – Deepak Kumar Guthikonda COLLAPSE Data mining as a Service (DMaaS) refers to computing as well as software infrastructure, which allows highly interactive of scientific data in the cloud (Tejedor et al., 2016). DMaaS is important because it allows computer users to run a highly advanced analysis. In addition, the DMaaS is important because it makes it easier to share scientific code and results. Also, it helps to increase access to scientific software. Similarly, DMaaS refers to a type of cloud service that offers businesses highly centralized storage for different data sources (Tejedor et al., 2016). It should be noted that the label “as-a-service” references a pay-per-use model, which does not require the customer for purposes of purchasing or managing infrastructure for data management.
In such an enterprise model, customers back up data into the DMaaS service provider. This is normally done through the installation of agents on the data sources that are being backed up. However, in the case of cloud data sources, it is important to note that a simple authentication process is normally required as the first step. It should be noted that DMaaS is normally an operating expense. It goes up and down based on how much service the client is consuming. It is technically possible to offer DMaas by use of on-the-premise infrastructure or a private cloud provided by the DMaaS vendor (Han & Leung, 2015). However, the entire infrastructure must be offered as well as managed by the DMaaS vendor in order to be considered a service. Even though it may be possible to carry out DMaaS in this manner, it is highly expensive to do so for cost and logistical reasons. As the name suggests, DMaaS must be conducted as a service. Therefore, it is not DMaaS if an organization must buy, install as well as maintain huge volumes of infrastructure for purposes of performing data management. It should be noted that the “as-a-service” signature should be in such a way that it should adhere to the traditions of the services that have been developed and defined the concept, including G-Suite and Office365, among others. For instance, none of these firms require their clients to install or even manage infrastructure, either virtual or physical for purposes of providing or consuming the service.
Firms that use such services normally inform the vendors of their particular needs, such as the number of users who should be registered and the amount of stores that should be allocated to every user. It should be noted that with this method, the infrastructure needed to offer that service will be automatically provided and managed directly by the vendor. It should be noted that DMaaS leverages cloud services for purposes of providing scalability, insight as well as accessibility to a firm’s numerous sources of data. In addition, the central data collection that is provided by DMaaS is leveraged for purposes of providing data protection as well as additional services. The data sources in DMaaS include application servers, file servers, and virtual machine (VM) databases. Also, most of the firms that seek DMaaS normally have data in one or more cloud providers (Han & Leung, 2015). Also, such firms have data in diverse devices, including laptops and mobile devices. In addition, companies that offer DMaaS also provide additional services, which may include proactive compliance, centralized search, and data analytics among others.
DMaaS has so many benefits. One of the benefits of using DMaaS is that it is cheaper compared to conventional data mining. Unlike conventional data mining where one has to buy different hardware and software, with DMaaS, an organization does not have to buy and install these devices and software (Han & Leung, 2015). Thus, using DMaaS is cheaper because the company will forgo the costs associated with buying and installing hardware and software devices. Secondly, DMaaS is beneficial compared to normal data mining because it enables the organization to forgo the cost of maintaining and updating the different software and hardware. This is because, with DMaaS, an organization does not invest in buying and installing hardware and software (Srivastava, Chandra & Srivastava, 2019).
Thus, the services of maintaining and updating the hardware and software are delegated to the DMaaS provider. References Han, Z., & Leung, C. K. (2015). FIMaaS: scalable frequent itemset mining-as-a-service on cloud for non-expert miners. In Proceedings of the 2015 International Conference on Big Data Applications and Services (pp. 84-91). Srivastava, S. K., Chandra, B., & Srivastava, P. (2019). The impact of knowledge management and data mining on CRM in the service industry. In Nanoelectronics, Circuits and Communication Systems (pp. 37-52). Springer, Singapore. Tejedor, E., Piparo, D., Mascetti, L., Moscicki, J., Lamanna, M., & Mato, P. (2016). Data Mining as a Service (DMaaS). In Journal of Physics: Conference Series (Vol. 762, No. 1, p. 012039). IOP Publishing. Reply peer post one : Peer post 2 : Katabattuni Week 5 COLLAPSE Data Mining in E-Commerce With the improvement of Internet, there is an online business fever everywhere on the world. Consistently, the online business framework creates a lot of data information which has for some time been portrayed by enormous information. With the approach of the large information age, looking for successful handling innovation and strategies to mine helpful data has become an earnest interest (Zhang, et. al., 2018). Data mining is one of the vital innovations to make enormous electronic business information assume a part.
Mining client gathering to acknowledge bunch information sharing, Mining client interest to acknowledge customized, scene, and pervasive information suggestion and push are necessary (Zhang, et. al., 2018). Investigate client unequivocal and certain information, too as cell phone, tablet and other utilize terminal discernment information, mining client profound interest (Zhang, et. al., 2018). More than 200 sorts of expectation strategies like sales forecast and a few exemplary forecast models have been created, which can be partitioned into two classes, emotional and target techniques (Huang, et. al., 2015). The abstract expectation technique depends on the experience of specialists who judge and gauge. It is emphatically emotional and adaptable. The target expectation technique utilizes crude information to fabricate models dependent on arithmetic and numerical measurements strategies (Huang, et. al., 2015).
It is reusable however not adaptable. The target expectation strategy incorporates essentially relapse examination and time arrangement investigation and these strategies utilize data mining information, building up a reusable model to foresee future deals (Huang, et. al., 2015). Web use mining typically contains two principal strategies: measurable examination, and further developed data mining calculations, for example, affiliation rules, successive examples, and bunching (Navarro & Torra, 2010). While the principal approach gives normal and merged factual assessments of the utilization, the subsequent methodology endeavors to distinguish utilization designs (Navarro & Torra, 2010). At the point when the information utilized by singular internet business merchants are connected to different data sets, nonetheless, issues of protection and privacy become up front (Stephen, 2006). To help address the protection concerns related with a brought together information store a few authorities have proposed changing to a dispersed methodology whereby each state would keep up ownership of its information and control access as per its individual laws (Stephen, 2006). Information security assurance is a significant issue for web-based business.
While arrangements like SSL encryption may assist organizations with security for classified information transmission, the protection entanglements of advertising information as a component of internet business are many (Stephen, 2006). Organizations need to treat information security appropriately and execute best practices, and they need to reevaluate their approaches on information access by others (Stephen, 2006). References: Huang, W., Zhang, Q., Xu, W., Fu, H., Wang, M., & Liang, X. (2015). A Novel Trigger Model for Sales Prediction with Data Mining Techniques. Data Science Journal, 14, 15. https://doi.org/10.5334/dsj-2015-015 Navarro, A. G. & Torra, V. (2010). Privacy-preserving data-mining through micro-aggregation for web-based e-commerce. Internet Research, 20(3), 366–384. https://doi.org/10.1108/10662241011050759 Stephen, E. F. (2006). Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation. Statistical Science, 21(2), 143–154. https://doi.org/10.1214/088342306000000240 Zhang, J., Zhang, C., & Yu, H. (2018). Research on e-commerce intelligent service based on Data Mining. MATEC Web of Conferences, 173, 3012. https://doi.org/10.1051/matecconf/201817303012 Peer post reply 2 :
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