The Classification Tree Is A Model That Uses Both Categorical And Numeric Inputs

Ann wants to describe the demographic characteristics of a sample of 25 individuals who completed a large-scale survey. She has demographic data on the participants’ gender (two categories), educational level (four categories), marital status (three categories), and community population size (eight categories).

After the end of WWI the roaring 20’s came to life, the economy was booming, and the U.S. felt a sense of pride. Sadly though all was not as grand as it seemed the land was stained with blood from racial issues. In the book The Learning Tree by Gordon Parks a coming to age story Newt Winger tells the tale of “how it feels to be black in the white man’s world.” He faces the struggle of the racial issue as he grows up in his little town of Cherokee Flats. The young twelve year old lives in a society where blacks are slowly being integrated but are still left with the shortest straw; not being allowed to eat or work in some places and the high schools black students are not allowed on the football nor the baseball teams while a certain teachers

These can be found in the Ashford Online Library by going to a research database such as JSTOR or ProQuest and searching for your topic. If available, narrow your search category to scholarly journals, including peer-reviewed and full-text documents only. At least three of your sources must be from peer-reviewed scholarly journals.

Knowledge attained wth the use of data mining techniques can be used to make innovative and successful decisions that will increase the success rate of health care sector and the health of patients. In this paper, the study of classification algorithms in data mining techniques and its applications are discussed. The popular classification algorithms used in healthcare domain are explained in detail. The open source data mining tools are discussed. The applications of healthcare sector using data mining techniques are studied. With the future development of information communication technologies, data mining will attain its full potential in the discovery of knowledge hidden in the health care organizations and medical

The data that was collected and analyzed came from Sweet Green in Boston, Massachusetts three times at various mid-day hours to ensure accuracy in the results. The initial unorganized date was written down in a blue book provided by the professor. The data was a consumption of every detail that could have impacted the original research question. Various bullet points were noted for the different factors and observations. Among these as example where outfit choices, gender, age range, cellphone use, friend groups, payment methods, and even drink choices. Upon completion of observations, the gathered data was organized and sorted into a table format that we identify as POET. POET is a more formal organization. It involves taking the informal

Today at Learning Tree, it was brought to my attention that Amiah and another little girl named Isabella (Amiah told me her name and that she was also in the same class), I believe it is, have been exchanging minor verbal altercations that were witnessed and dealt with by the classroom staff. However, it became an issue today when Isabella told her parents Amiah had been bullying her. The staff member informed me of this and that on the contrary of what the child said; it was more mutual words being exchanged with the other child initiating the conversation. I just wanted to inform you on what was going on and that I have instructed Amiah and her Learning Tree teacher agreed for the afternoon program to move away from her at all times to cut

View of Data: I think this approach was too hard to categorize the data. I’m not sure, what data can represent the values, attitudes, or beliefs code? Moreover, I think the contents in the transcription are not enough for coding.

The level of measurement of the dependent variable influences the choice of statistical tests used to analyze the data. The simplest level of measurement is Nominal and the numbers as well as words and letters can be used to classify the data. An example is the two different genders, the female gender is classified as F, and the male gender is classified as M. The ordinal level of measurement showed in order such as rank. the ratio between any two types of rankings is different along the scale. The interval level of measurement does not only classify, place things in orders but it specifies that the equivalent along the scale from low interval to high interval. The ratio measurement is complex and can have a value of zero. In the ratio level of measurement, the divisions between the points have distance, and placed according to their size. (Grove, Burns, and Gray,

Predictive analytics will have a colossal impact in the field of healthcare, especially where there are basic courses of action of unbalanced and confined data. New systems being passed on through predictive examination will allow those including star's work environments, medical centers and course of action relationship to take a gander at this data to see how it can best guide medical key force.

The variable GPA was measured by the question “What is your current overall grade point average?”. This was an open-ended question were the person being interviewed was encouraged to answer with their current grade point average. After conducting the interviews and reviewing the master data set, this variable was broken down into two categories. The two categories were coded as high GPA and low GPA and was broken down by splitting the data as close to a 50/50 split as possible. High GPA represented grade point averages from 3.5-4.0 and low GPA represented grade point averages from 2.0-3.4.

In today’s technological world patients are choosing where they receive their care based on research and public access to hospitals quality of care numbers. Hospitals are competing with other hospitals for patients. In order to attract patients hospitals are improving their quality of care by providing safe and efficient care. Advancements in Medical Technology has made it possible for Health care providers to better diagnose and treat their patients, one of those medical advancements is conversion of International Statistical Classification of Diseases and Related Health Problems 9th edition (ICD-9) to International Statistical Classification of Diseases and Related Health Problems 10th edition (ICD-10).

The quantitative data collected using the SOCARP tool, the data collected is ordinal but due to being recorded in ten second intervals is recorded as interval data. As the ordinal data was recorded as interval data it was considered as continuous for the purpose of this analysis. Observations of the data were found to be coded in such a way that the data could be treated as parametric due to its coding aligning itself with Rice’s (2007) central limit theorem and not depending on other values.

There are five steps involved in creating a decision tree. The first step is to identify and set limits to the problem, for example whether or not the gallbladder of a person without symptoms should be removed. The second step is to diagram the options. This can be done by starting with the patient’s current clinical status, then laying out the different options and the outcomes that could come from each option. The third step is to obtain information on each option. In this step, the possibility of each outcome occurring should be assessed and then the outcomes should be separated into desirable and undesirable outcomes. The fourth step is to compare utility values and the fifth step is to perform sensitivity analysis (97-98).

Our approach to classification of user’s cancer risks will use a larger number of dimensions to calculate risk of different types of cancers than other similar analytic techniques. The data used for this project will come from the Surveillance, Epidemiology, and End Results (SEER) data from the National Cancer Institute. SEER's extensive datasets allow many different analyses to be done from general population cancer statistics (Siegel) to specific medical decision making applications such as whether a certain treatment for prostate cancer would be beneficial (Culp). Other approaches have utilized SEER datasets and supervised classification methods to develop survival prediction models for colon cancer (Al-Bahrani) and chart survival curves for different treatments for lung cancer (Owonikoko). However, these previous approaches have focused on one type of cancer and are therefore less comprehensive than the tool we are seeking to develop. We also plan to implement an interactive, user-friendly visualization tool that allows for quick interpretation of the results. The user would receive a list of cancer they are most

Machine learning has been gaining popularity in healthcare because of its ability to use existing mathematical models and apply them to new instances of an established concept in other data. This ability to automatically identify patterns in data is one of the major reasons for the potential of machine learning in healthcare settings—as well as its ability to fill in the gaps of expert knowledge, adjust for exceptions, and efficiently handle massive amounts of data [1].