Data Mining in Healthcare

Data Mining in Healthcare

Here is the suggested layout and the grading criteria for the Data Mining Paper

Title page (running head, title of paper, page numbers, student(s) names, date, course) -grade counted in APA format below

Introduction (abstract not required). Be sure to include introduction to content and purpose statement for the paper – 5 points

Data Mining vs Statistics – summary of how data mining and statistics are similar and different from each other. You should discuss at least 3 similarities and/or differences. – 15 points

Strengths and Weaknesses of Data Mining – summary of strengths and weaknesses of data mining. You should discuss at least 3 strengths and/or weaknesses – 15 points

Stages of a Data Mining Project 15 points

Strategies Used in Data Mining – strategies used in data mining. You should discuss at least 3 (be sure to refer to the worked examples in chapters 5-14 for additional information) – 15 points

Importance of Data Mining in Health Care – now that the course is finished, why do you think data mining is important in health care IT? It is ok to use first person here. – 20 points

Conclusion – briefly summarize the main points of the paper – 5 points

References – age of references is not a factor. You should include your textbook as well as at least 4 other outside sources. EACH section of the paper should include references (some references will likely be included in more than one section). – 5 points (1 point for each reference. Quality of references counts here. APA is counted below)

APA Format/Grammar/Spelling – Title page format, Paper should be written in Times New Roman 12 pt, 1″ margins, double spaced, page numbers, section headings are present, references should be in APA format, no spelling and/or grammar errors

Length – there is NO page limit on this paper. Quality is graded vs quantity. Simply be sure you have covered each of the above areas and don’t focus on page limits. Be sure to not simply list things in the above areas – discuss them thoroughly.


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  • Data Mining in Healthcare
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