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of Computer Engineering in Research Trends (IJCERT)

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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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Risk Projection for Readmission of Congestive Heart Failure Patients on Big Data Solutions

Sreeja.D, N.Ananda Reddy, , ,
1: PG Student, Dept of Computer Science And Engineering, Siddartha Educational Academy Group of Institutions,C. Gollapalli, Tirupati,AP,India; 2: Assistant Professor, Dept of Computer Science And Engineering, Siddartha Educational Academy Group of Institutions,C. Gollapalli,Tirupati,AP,India

Big Data is a collection of data that is large or complex to process using on-hand database management tools or data processing applications. It is becoming very difficult for companies to store, retrieve and process the ever-increasing data. In other words we can say, Big Data is term given to humungous amount of data which is difficult to store and process. The issue lies in using the traditional system is, how to store and analyze Big Data. Risk prediction involves integration of clinical factors with socio-demographic factors like health conditions, disease parameters, hospital care quality parameters, and a variety of variables specific to each health care provider making the task increasingly complex. Unsurprisingly, many of such factors need to be extracted independently from different sources, and integrated back to improve the quality of predictive modeling. Such sources are typically voluminous, diverse, and vary significantly over the time. This project takes Apache Hadoop, an intrinsic part for storing, retrieving, evaluating and processing huge volumes of data for processing effectively. In this work, we study big data driven solutions to predict the 30-day risk of readmission for congestive heart failure (CHF) incidents. We will predict this process by using Logistic Regression and Naive Bayes classification on the basis of data collected from patients. The results are remarkable after the comparison between the two techniques and presented through confusion matrix.

Sreeja.D,N.Ananda Reddy."Risk Projection for Readmission of Congestive Heart Failure Patients on Big Data Solutions". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.8, Issue 10,pp.175-178, October - 2021, URL :,

Keywords : Bigdata, Congestive heart failure, Congestive Cardiac Failure, Risk-Standardized Readmission Rate

[1] Krumholz H. M., Normand S. L. T., Keenan P. S., Lin Z. Q., Drye E.E., Bhat K. R.,  Wang Y. F., Ross J. S., Schuur J. D., and Stauer B. D..Hospital 30-day heart failure  readmission measure methodology.Report prepared for the Centers for Medicare & Medicaid  Services. 
[2]  Amarasingham R, Moore BJ, Tabak YP, Drazner MH, Clark CA, Zhang S, Reed WG,  Swanson TS, Ma Y, Halm EA. An automated model to identify heart failure patients at  risk for 30-day readmission or death using electronic medical record data. Journal of Medical Care, 10:981-988, Feb.  2010. 
[3] An automated model to identify heart failure patients at risk for 30-day readmission or  death using electronic medical record data. 
[4] Koelling T. M., Johnson M. L., Cody R. J., and Aaronson. K. D.Discharge education  improves clinical outcomes in patients with chronic heart failure. Circulation, 111(2):179- 185, Jan. 2005. 
[5] Impact of prior admissions on 30-day readmissions in medicare heart failure inpatients. 
[6] Meadam N., Verbiest N., Zolfaghar K., Agarwal J., Chin S., Basu Roy S., Teredesai A.,  Hazel D., Reed L., Amoroso P. Exploring Preprocessing Techniques for Prediction of Risk of  Readmission for Congestive Heart Failure Patients. In Data Mining and Healthcare  Workshop, in conjunction with the 19th ACM SIGKDD Conference on Knowledge  Discovery and Data Mining (KDD), 2013.

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