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3-2 Final Project Milestone One: Introduction Anastasia Bolinaga Southern New Hampshire University DAT 510: Foundations of Data Professor Ali Motamedi, Ph.D September 11, 2022
Background Environment: Health Insurance Current data analysis initiatives : improve lifestyle choices, including management of weight and stress for insurance policy holders with history of heart attacks to avoid reoccurrence Data analytics tools : RapidMiner or R – Logistic Regression (North, 2012)
Data Sources Attribute Data type Comment Age Numeric years rounded to the nearest whole year Marital_Status Categorical 0–Single, never married; 1–Married; 2–Divorced; 3– Widowed. Gender Categorical 0 - female; 1 - male Weight_Category Categorical 0 - normal weight range; 1- overweight; 2 - obese Cholesterol Numeric recorded at the time of the treatment for the most recent heart attack Stress_Management Categorical stress management course attendance: 0 - no; 1 - yes. Trait_Anxiety Numeric Test of natural anxiety result on a scale of 0 to 100 2nd_Heart_Attack* Nominal ‘yes’ for individuals who have suffered second heart attacks, and ‘no’ for those who have not * only in training dataset
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Data Needs Claims database* Clinical database** Pharmacy database** *current state **future state Data Pool Attributes Claims database Demographics, medical codes Clinical database Electronic medical records (EMR) information, surveys Pharmacy database Adherence to medication therapy
Data Analytics Initiative: Data Warehouse Design • Combine databases • Extract, transform and load data • Data analysis and decision-making
References Abai, N. H., Yahaya, J. H., & Deraman, A. (2013). User requirement analysis in Data Warehouse Design: A Review. Procedia Technology , 11 , 801–806. https://doi.org/10.1016/j.protcy.2013.12.261 Alexander, C. A., & Wang, L. (2017). Big Data Analytics in heart attack prediction. Journal of Nursing & Care , 06 (02). https://doi.org/10.4172/2167-1168.1000393 Anderson, R. P., Jin, R., & Grunkemeier, G. L. (2003). Understanding logistic regression analysis in clinical reports: An introduction. The Annals of Thoracic Surgery , 75 (3), 753–757. https://doi.org/10.1016/s0003-4975(02)04683-0 North, M. (2012). Chapter 9: Logistic Regression. In Data mining for the masses (pp. 141–156). essay, A Global Text Project Book Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications , 17 (8), 43–48. https://doi.org/10.5120/2237-2860
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