Health Data Management And Knowledge Discovery Case Solution And Analysis, HBR Case Study Solution & Analysis of Harvard Case Studies

Health Data Management And Knowledge Discovery Case Solution

Introduction

This case illustrates the importance of determining the statistical results of the healthcare perspective and identify the validity of the results through the generated accuracy and highlighted errors. Under the scenario, it has been analyzed that the data would be determined through the use of Wake, a statistical inference software, which enabled the data to expand the results into various forms like mean, standard deviation, expected accuracy of the results with conditional and UN-conditional way.

Moreover, it analyzed the errors in the form of UN-specified and specified one. Furthermore, it would also be determined that decision tree of the related results by the use of J 48 algorithm and other sort of elements. The reprocessing of Wake composed of the expected results that the data would incur based on the information and generate the mean and visualization. However, the visualization composed of the results that utilize the space between the data and also identify the probability to generate the errors.

Therefore, it shows that there are different aspects to test the data of Health management like number of pregnancy, age, blood pressure etc. Thus, in order to define the elements of Wake, the source would be identified as the differences between the medical components like the defined above.

There are different processes to generate the results. The first process is the reprocess that collects the data and generate the visualization figures, the second is the classification of the generated results, which would be able to implement J 48 algorithm and other source of codes. Also, it would determine the accuracy and errors of the selected data being performed. In addition to that, implement the visualization errors and decision tree accordingly.

The third process of Wake is subjected to the use of Clusters, which would divide the set into parts and generate the cluster instances. The forth category shows the association category that defines the rules of the related data that should be performed accordingly. The fifth element uses the selected attributes that are the main components to analyze the data and process with the define terms and conditions.

The final part is the visualization of the values that would be able to generate the outcomes in the form of dots. However, if the dots are linked with each other, then it can be said that the values are close to each other. If the dots are far from each other then the values are not matched or close to each other.

With all these functions, it can be seen that the expected outcome would provide the basic structure of decision tree and its accuracy and errors specified through the use of selected data. The data is collected from the source of Health and knowledge discovery. The data defines many variation of the results with regard to the changes in the value performed under each interval. The final result would determine and define the association rules based on what the results estimate and predicts.

Data preparation

In the section of data preparation, it has been identified that the source would come from the Health and data management data, that provides various results in the form of frequencies and distribution of each period and identify the visualization rate in the form of graphs. Therefore, to include the data into the Wake, the reprocess element of the software would be performed to collect data and generate the means and frequencies of each data.

The data illustrates nine form of Health care issues and indicators that would be predicted in the same scenario in the future. The results indicate different level of visualization rates and their comparison with other. It has been analyzed that the overall picture shows statistical values in the form of mean and frequencies, as well as it shows determining the differences of means with each other with regard to the data sources being performed.

Therefore, the first step to generate the values is to put the values in the reprocess and analyze the results of various frequencies in order to determine the best attributes that would produce the perfect decision tree process and association rules. It would also identify the expected accuracy and errors subjected to variations in the process.

Health Data Management And Knowledge Discovery Harvard Case Solution & Analysis

It also shows the relative changes in the results based on the frequencies being identified. Thus, from the data perspective, it is concluded that preparing an appropriate data would be able to generate the proper results based on what the Wake would be able to show the decision tree and the validity of the results............

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