Are there any specific data mining techniques you would not use?

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Nov 13, 2022


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Topic 5 DQ 2
Nov 10-13, 2022
Using the clinical question you identified in the previous discussion question, determine the individual components to that question and pinpoint the location in the hypothetical database where the information you require will be extracted. Below is my first answer to topic 5 #1
#1 My first homework: Select a specific clinical problem and post a clinical question that could potentially be answered using data mining. Identify data mining techniques you would apply to this challenge and provide your rationale. Are there any specific data mining techniques you would not use? Support your decision.
Clinical problem: CHF readmission
Heart failure, a complex syndrome that develops in the terminal stage of cardiovascular disease, seriously threatens patient life and health. More than 26 million people worldwide are hospitalized annually for acute heart failure, with a one-year mortality rate of 20%–30% and an increased risk of rehospitalization that creates huge public health and financial burdens (Lu et al., 2021).
Cluster analysis is done by algorithms that group data points together based on similarity. This can be used to find groups of customers with similar characteristics. For example, patients are coming back to ER with the same similarities: shortness of breath, chest pain, weakness, leg swelling, fluid overload, etc.
Data mining can help reduce costs by providing a competitive advantage and Cluster analysis can help identify patterns and groupings in data. This can be used to segment customers, find new markets, or optimize marketing campaigns. “spending a lot of time and money looking very closely at the various ways that big data might impact their bottom line.” To avoid Medicare penalties associated with the Affordable Care Act, for example, many facilities are using big data in an attempt to minimize readmissions. The same goes for HAIs or hospital-acquired infections. The data, through predictive analytics, is giving such facilities answers and they’re adjusting their practices accordingly (Hayhurst, 2015). Heart failure, likewise named congestive heart failure, happens when the heart can’t sufficiently direct blood to address the needs of the body. Most of the risk indicators for the failure of the heart incorporate hypertension, an earlier heart attack, stoutness, smoking, liquor misuse, vitamin lacks, lack of sleep, overwhelming metal poisonous quality, non-healthy eating like fats of animals, and being inactive. Failure of the heart is more typical in the middle of individuals beyond 65 years old, individuals with more weight, and individuals who had past heart failure.
Data mining with the new technology available can easily put customers’ information and privacy at risk. Which may compromise confidentiality and privacy. One way for this to happen is through data aggregation where data is accumulated from different sources and placed together so that they can be analyzed. The combination of big data and deep learning is a world-shattering technology that can make a great impact on any industry if used in a proper way (Maragatham et al., 2019). I have attached a graphic that I may be able to use in regard to CHF or early ambulation after surgery. I will use data mining and the factors risks of CHF to be able to identify and use it properly to help readmission.
Hayhurst, C. (2015). Mining for Answers from Big Data. Biomedical Instrumentation & Technology, 49(2), 84
Lu, X., Xin, Y., Zhu, J., Dong, W., Guan, P., Li, Y., & Li, Q. (2021). Diuretic Resistance Prediction and Risk Factor Analysis of Patients with Heart Failure During Hospitalization. Global Heart, 17(1).
Maragatham, G., & Devi, S. (2019). LSTM Model for Prediction of Heart Failure in Big Data. Journal of Medical Systems, 43(5), 1.


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