Project Proposal (Data Mining Healthcare)
Project Proposal (Data Mining Healthcare)
Team Project Proposal
Learning Objective (s):
After developing the Project proposal students will be able to:
- Identify a healthcare problem to address using data mining technique (CO1)
- Explore data for data mining (CO2)
- Identify Supervised Models for addressing the problem (CO3)
- Identify Unsupervised Model for addressing the problem (CO 4)
The team Project proposal is due on Saturday by 11.59pm CT. This is a group/team submission. One of the team members will submit the proposal on teams behalf and all team members can see the submission. To get a better idea of the requirements for the project, kindly refer to slides and corresponding Recording 1 available under Team Project Implementation Information
File Naming convention – Rename the file to \”Teamname.HCI576 Project Proposal\” before submission.
Instructions:
The second step of the team project is to develop a project proposal.
So, far we have learned the concepts of data mining including its application to address diverse healthcare problems. Additionally, we have learned the concept of SEMMA to address a problem using the data mining. The goal of the project is to identify and define a problem team want to solve using the data mining techniques. For example, the hospital readmissions data to understand the trends that what factors can be grouped together to categorize the type of patients or disease. This will be helpful for the hospitals to meet patient requirements and well being. Also, the hospital readmissions data can be analyzed to identify the most significant factors in predicting the hospital readmission rate (numerical target) or predicting whether the hospital readmission will be high or low (categorical target) next week or month. Team can refer to following articles to understand some of the phenomena that can be addressed using the data mining techniques:
-
- Singh, N., Kanthwal, A., Bidhuri, P., & Munnolli, A. V. (2020). Using Individual Decision, Economic, and Health Status Data to Predict Health Checkup Behavior (Links to an external site.). Journal of the Midwest Association for Information Systems| Vol, 2020(1), 32.
- Alnsour, Y., Hadidi, R., & Singh, N. (2019). Using Data Analytics to Predict Hospital Mortality in Sepsis Patients (Links to an external site.). International Journal of Healthcare Information Systems and Informatics (IJHISI), 14(3), 40-57.
- Singh, N., Kanthwal, A., Bidhuri, P., & Munnolli, A. V. (2018). Role of Decision Making in Predicting Health Behavior (Links to an external site.). MWAIS 2018 Proceedings. https://aisel.aisnet.org/
mwais2018/15/
Subtitle or Section:
To work on the project, teams will identify the problem, obtain the data, and define the scope of the problem.
There should be at least two problem questions that team need to identify within the scope of the problem. One of the questions need to be addressed using predictive analysis (using supervised techniques) and the other using exploratory analysis (unsupervised techniques).
Team will use visualization for data exploration and preparation. Then, team will implement several supervised and unsupervised data mining techniques to solve the identified problem questions. Appropriate performance evaluation metrics will be used to evaluate the performance of these supervised and unsupervised model to establish the significance of the developed models with highest accuracy. Finally, scoring the predictive models (supervised models) on new data will be performed. Scoring plays a crucial role in data mining as it allows in confirming the reliability of the developed predictive model by deploying the model on new data.
To assist teams in solving a feasible and interesting problem using several data mining techniques, each team should obtain the instructor’s approval regarding the scope and nature of the problem, the data set, and the nature of the analysis. To start with, teams must submit a project proposal by answering questions (as much as you know) using the attached template (HCI576 Team Project Proposal-Template.docx Download HCI576 Team Project Proposal-Template.docx ). Identifying a problem that interests you and you would like to address is crucial in data mining. The goal of the project proposal is to identify the problem, obtain the data, and define the scope of the problem. This will also include identification of tentative predictor and target variables.
Potential data sources can include, but are not limited to the Internet (e.g., search for “datasets for data mining†), current or past employers (with permission), public databases, university datasets, datasets that you collected in the past. Some sources of publicly accessible datasets are as follows. You might need to create a free profile on some of these websites to access the content. Using university email ids to create the profile might provide additional benefits.
- Google Dataset SearchLinks to an external site.
- https://www.cdc.gov/data.
htmlLinks to an external site. - Data | HealthIT.govLinks to an external site.
- http://www.ahd.comLinks to an external site.
- https://www.healthgrades.com/
Links to an external site.
Submission Instructions:
The project proposal bears its own points towards the final grades. Kindly refer to syllabus for the weightage of project proposal.
While working on the project proposal if you have any questions please email me and if needed I can schedule a quick zoom call with the team to discuss the data sets. I can meet during the weekdays, in evenings or over the weekends.
Grading Rubric
Your assignment will be graded according to the grading rubric attached to this assignment.
Rubric
HCI576 Project Proposal Rubric
| Criteria | Ratings | Pts | |
|---|---|---|---|
| This criterion is linked to a Learning OutcomeRename file | 1 pts
Meets requirement Naming convention \”Teamname.HCI576 Project Proposal\” is used
0 pts Does not meet requirement Naming convention \”Teamname.HCI576 Project Proposal\” is NOT used.
|
1 pts | |
| This criterion is linked to a Learning OutcomeCover page | 1 to >0.5 pts
Meets Requirement Project Title is included. Team Name is included. Team Member names and UIS emails are included.
0.5 to >0.0 pts Partially Meets Requirement Some of the criteria in Meets Requirement are missing
0 pts Does not meet requirement All of the criteria in Meets Requirement are missing
|
1 pts | |
| This criterion is linked to a Learning OutcomeQuestion 1: Problem Definition | 2 to >1.0 pts
Meets Requirement Problem is identified and scope of the problem is discussed. Also, the relevance or importance of problem for a data mining project is discussed.
1 to >0.0 pts Partially Meets Requirement Some of the criteria in Meets Requirement are missing
0 pts Does not meet requirement All of the criteria in Meets Requirement are missing
|
2 pts | |
| This criterion is linked to a Learning OutcomeQuestion 2: Tentative Question | 2 to >1.0 pts
Meets Requirement Tentative questions for both Exploratory and Predictive analysis are developed.
1 to >0.0 pts Partially Meets Requirement Some of the criteria in Meets Requirement are missing
0 pts Does not meet requirement All of the criteria in Meets Requirement are missing
|
2 pts | |
| This criterion is linked to a Learning OutcomeQuestion 3.1: Data Source | 1 to >0.5 pts
Meets Requirement Exact source of data set is provided to download the data. URL is provided if applicable. OR Details of owner and consent of use by owner of data is provided if primary data is used.
0.5 to >0.0 pts Partially Meets Requirement Some of the criteria in Meets Requirement are missing
0 pts Does not meet requirement All of the criteria in Meets Requirement are missing
|
1 pts | |
| This criterion is linked to a Learning OutcomeQuestion 3.2: Dataset Description and Relevant Literature | 2 to >1.0 pts
Meets Requirement Brief description of dataset is provided. Further, the support from literature is provided to identify the gap and to address the problem in a different way.
1 to >0.0 pts Partially Meets Requirement Some of the criteria in Meets Requirement are missing
0 pts Does not meet requirement All of the criteria in Meets Requirement are missing
|
2 pts | |
| This criterion is linked to a Learning OutcomeQuestion 3.3: Target Variable Details | 1 to >0.5 pts
Meets Requirement Tentative Target variable is identified for predictive analysis. Also, the data type and description of target variable is provided.
0.5 to >0.0 pts Partially Meets Requirement Some of the criteria in Meets Requirement are missing
0 pts Does not meet requirement All of the criteria in Meets Requirement are missing
|
1 pts | |
| This criterion is linked to a Learning OutcomeQuestion 3.3: Predictor Variables Details | 2 to >1.0 pts
Meets Requirement Tentative Predictor/input variables are identified for exploratory and predictive analysis. Also, the data type and description of each predictor variable is provided.
1 to >0.0 pts Partially Meets Requirement Some of the criteria in Meets Requirement are missing
0 pts Does not meet requirement All of the criteria in Meets Requirement are missing
|
2 pts | |
| This criterion is linked to a Learning OutcomeQuestion 4: Timetable (Project Plan) | 2 to >1.0 pts
Meets Requirement Project plan includes all the activities/steps (SEMMA approach) of data mining process as well as milestones of the project.
1 to >0.0 pts Partially Meets Requirement Some of the criteria in Meets Requirement are missing
0 pts Does not meet requirement All of the criteria in Meets Requirement are missing
|
2 pts | |
| This criterion is linked to a Learning OutcomeTeam Collaboration | 1 pts
Meets Requirement Each team member participates in team collaboration using File Exchange Folders and Group Discussion Board/chats in the chosen collaboration platform
0 pts Does not meet requirement All of the criteria in Meets Requirement are missing |
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