Verified Document

Data Mining To Identify Trends In Community Health Essay

Difference between Data Mining and Data Analytics

Data mining and data analytics are often used interchangeably in the health and nursing fields, but they actually represent distinct processes with unique goals and methodologies. It is helpful to know the differences between the two can because understanding how they work and what they are used for can improve the implementation of data-driven decision-making in healthcare.

Data mining is the process of discovering patterns and relationships within large datasets (Gupta & Chandra, 2020). Involved in the process of data mining is the use of algorithms and statistical models that can be used to identify some of the hidden patterns, trends, and correlations within the data that might not be immediately apparent to the user. In health and nursing, data mining can identify risk factors for diseases, identify trends in community health, predict patient outcomes, or uncover patterns in patient care that could facilitate development of improved treatment methods. Techniques of data mining can be things like clustering, classification, or association rule learningand all of these are commonly used in data mining to analyze datasets so as to extract meaningful information (Gupta & Chandra, 2020).

Data analytics is a different in that as a field it involves many varieties of methods useful in looking at datasets in support of decision-making (Sarker, 2021). For example, it involves descriptive, predictive, and prescriptive analytics. Descriptive analytics can be used to summarize historical data to help one understand what has happened in the past. Predictive analytics uses statistical models and machine learning techniques to make forecasts based on historical data. Prescriptive analytics goes a step more by helping one make recommendations for desired outcomes based on the data. In nursing, data analytics can help with things like monitoring patient vitals, predicting hospital readmissions, or making the most of staffing levels to achieve the best quality patient care.

References

Gupta, M. K., & Chandra, P. (2020). A comprehensive survey of data mining.International

Journal of Information Technology,12(4), 1243-1257.

Sarker, I. H. (2021). Data science and analytics: an overview from data-driven smart computing,

decision-making and applications perspective.SN Computer Science,2(5), 377.

Cite this Document:
Copy Bibliography Citation

Related Documents

Data Mining
Words: 1427 Length: 4 Document Type: Research Paper

Data Mining Determine the benefits of data mining to the businesses when employing: Predictive analytics to understand the behaviour of customers "The decision science which not only helps in getting rid of the guesswork out of the decision-making process but also helps in finding out the perfect solutions in the shortest possible time by making use of the scientific guidelines is known as predictive analysis" (Kaith, 2011). There are basically seven steps involved

Data Mining in Health Care Data Mining
Words: 1003 Length: 3 Document Type: Essay

Data Mining in Health Care Data mining has been used both intensively and extensively in many organizations.in the healthcare industry data mining is increasingly becoming popular if not essential. Data mining applications are beneficial to all parties that are involved in the healthcare industry including care providers, HealthCare organizations, patients, insurers and researchers (Kirby, Flick,.&Kerstingt, 2010). Benefits of using data mining in health care Care providers can make use of data analysis in

Data Mining Businesses Can Receive Many Benefits
Words: 1387 Length: 4 Document Type: Essay

Data Mining Businesses can receive many benefits from data mining. Which benefits they receive, however, can also depend on the way in which their data mining is undertaken. Predictive analytics are used to understand customer behavior, and businesses use the behavior of the customer in the past to attempt to determine what the customer will do in the future (Cabena, et al., 1997). While it is not an exact science, many

Data Warehousing and Data Mining
Words: 2013 Length: 6 Document Type: Term Paper

Data Warehousing and Data Mining Executive Overview Analytics, Business Intelligence (BI) and the exponential increase of insight and decision making accuracy and quality in many enterprises today can be directly attributed to the successful implementation of Enterprise Data Warehouse (EDW) and data mining systems. The examples of how Continental Airlines (Watson, Wixom, Hoffer, 2006) and Toyota (Dyer, Nobeoka, 2000) continue to use advanced EDW and data mining systems and processes to streamline

Data Mining, a Process That Involves the
Words: 1271 Length: 4 Document Type: Essay

Data mining, a process that involves the extraction of predictive information which is hidden from very large databases (Vijayarani & Nithya,2011;Nirkhi,2010) is a very powerful and yet new technology having a great potential in helping companies to focus on the most important data in their data warehouses. The use of data mining techniques allows for the prediction of trends as well as behaviors thereby allowing various businesses to make proactive

Data Mining Evaluating Data Mining
Words: 3527 Length: 10 Document Type: Thesis

The use of databases as the system of record is a common step across all data mining definitions and is critically important in creating a standardized set of query commands and data models for use. To the extent a system of record in a data mining application is stable and scalable is the extent to which a data mining application will be able to deliver the critical relationship data,

Sign Up for Unlimited Study Help

Our semester plans gives you unlimited, unrestricted access to our entire library of resources —writing tools, guides, example essays, tutorials, class notes, and more.

Get Started Now