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Cardiovascular Disease Assignment On Cardiohealth Project

 

Question

Task: Develop a project report describing the life cycle of the project. In you report, you must also provide a stakeholder register identifying the importance and the interest of each stakeholder. Present a detailed work breakdown structure in your report.

Answer

1. Introduction
The impact of “CVDs” or “Cardiovascular Diseases” is deadly. Studies used to prepare this cardiovascular disease assignment have shown that it will be one of the leading causes of dead in the near future. The social influence determines health diseases globally (Kreatsoulas & Anand, 2010). The doctors and scientists are now inventing new ways to detect the cause factors of “Cardiovascular Diseases” to prevent risks. The leading causes behind “CVDs” are obesity, lack of physical exercise, and the negative impact of alcohol consumption (Rahman, et al., 2017). The following ‘CardioHealth’ project examined in the cardiovascular disease assignment will detect the “Cardiovascular Diseases” by using an ECG machine among the patients to analyze their conditions.

2. Project background
The patients with high level “CVDs” need early diagnosis process in order to recover soon. There are plenty of medical tests that help in detecting “Cardiovascular Diseases” according to their category. These are including “ECG test”, “ECG monitoring test”, “MRI test”, “CT scan”, “Echocardiogram,” and stress test (Mayo Clinic, 2018). Dr. Ben Williams, who is also a physicist and mathematician, prefers mathematical models and bio-medical statistics to detect “Cardiovascular Diseases” in the ‘CardioHealth’ project. Hearth surgeon Prof. John Price has worked and published various research papers related to the different areas of “Cardiovascular Disease.” Both of them have worked on a project called ‘Machine Learning’ to detect various hearth diseases by using “ECG test” data.

The cardiovascular disease assignment explores the ‘CardioHealth’ which consists of “ECG” or “Electrocardiogram” tests of various patients with “Cardiovascular Diseases” to analyze the disease factors and their potential remedies. The “ECG test” evaluates the heart rate of a patient. The abnormal heart rhythm will indicate hearth disease or problem. The ‘Machine Learning’ detection method is one of the most common practices in the present scenario to predict future heart disease risks of a patient (Nichenametla, et al., 2018). Dr. Ben Williams and Prof. John Price have performed the “ECG test” by using ‘Machine Learning Algorithms’ on hundreds of patients with “Cardiovascular Diseases”. They have evaluated a technique that ‘Machine Learning Algorithms’ can predict heart disease risks and factors, and start the diagnosis process early (Maji & Arora, 2019). They have contacted with other researchers, doctors, and technical supporters to work on the ‘CardioHealth’ project to develop an application to predict heart diseases by using ‘Machine Learning Algorithms’ and store the “ECG test” data on the personal cloud.

3. Project Life Cycle

‘CardioHealth’ Project Lifecycle

Initiation

· Dr. Ben Williams and Prof. John Price have observed hundreds of “ECG test” reports with ‘Machine Learning Algorithms’ technique to predict “Cardiovascular Diseases”.

· The ‘Machine Learning Algorithms’ technique helped them in predicting some heart diseases by analyzing the “ECG test” data (Kreatsoulas & Anand, 2010).

· The analysis detects future heart problems of a patient to start the diagnosis process early and effectively.

· They have discussed they analysis results with other doctors, technology expertise and government officials to invent a new application to provide and store “ECG test” results of different patients.

Planning

· The doctors, software developers and government officials have decided to start a new “ECG test” service business by implementing the ‘Machine Learning Algorithms’ technique.

· They have named the project as ‘CardioHealth’ and propose a business model as mentioned in the cardiovascular disease assignment.

· After several meetings with the interested doctors, technicians and government officials, Dr. Ben Williams and Prof. John Price have contacted with an investor for funding.

· Investor Arthur Davis have funded the ‘CardioHealth’ project and secure a ‘governmental biomedical’ start-up grant.

· The ‘CardioHealth’ project has also assigned an IT project consultancy company called “Information System Services” (ISS) to divide the project stakeholders according to specific categories.

· The business partners and stakeholders have predicted the project duration from 1st January 2020 to 31st December 2020.

Implementation

· The process outlined in the cardiovascular disease assignment starts with conducting a “ECG test” of a patient by qualified medical personnel and sent the result to a “Cloud-based” application (Dhar, et al., 2018).

· The stored “ECG test” data allows the “Cloud-based” application to process the data to conduct ‘Machine Learning Algorithms’ technique (Safdar, et al., 2018).

· The ‘Machine Learning Algorithms’ consists result predicts heart problems and sent the prediction result to the Cardio specialists for further analyzation (Fatima & Pasha, 2017).

· The Cardiologist will upload the analysis result to the cloud and stored the data with its correspondent files.

· The report will also can be assessible by the general practitioners for further analysis.

· The patients can assess the “ECG test” results and its analysis data from the cloud. 

Closure

· The “ECG test” service cloud application will be tasted by at least two hundred patients to test the flexibility of the application.

· The Cardiologist will help in analyzing the “ECG test” results within a specific time to make the process convenient.

· The government health sectors will encourage the doctors and patients to use this application effectively to check its mechanism, and detect error to resolve before the launch.

· The whole ‘CardioHealth’ project illustrated in the context of cardiovascular disease assignment has to be completed within 31st December 2020 after testing the application for multiple times to confirm its flexibility.

4. Kick-off meeting documents
4.1 Stakeholder Identification

Stakeholder

Name

Project initiator and developer

Dr. Ben Williams and Prof. John Price

Project Investor

Arthur Davis

Chief Information Officer

Michael Flynn

Head of the Application Group

Padma Tilak

Head of the Artificial Intelligence and Machine Leaning Group

Bruce Henry

Head of the Database Group

Helen Mills

Head of the Cloud Computing Group

John Fuller

4.2 Roles and responsibilities of the case on cardiovascular disease assignment

Stakeholder name

Role

Responsibilities

Communication mode

Dr. Ben Williams and Prof. John Price

Project Initiator and Developer

Frame the project mechanism and ‘Mechanism Learning Algorithms’ related analysis development

Email and face-to-face

Arthur Davis

Project Investor

Fund the ‘CardioHealth’ project and apply a biomedical start-up grant from the government

Email and face-to-face

Michael Flynn

Chief Information Officer

Monitoring the application work under the “CardioHealth’s” IT project managing consultancy company ‘ISS’ 

Email, face-to-face and telephonic conversation

Padma Tilak

Head of the Application Group

Involved in the control of the development of ‘CardioHelth’ application appointed by the ‘ISS’ noted herein cardiovascular disease assignment

Email, face-to-face and telephonic conversation

Bruce Henry

Head of the Artificial Intelligence and Machine Leaning Group

Supervise the implementation of ‘Data Mining’ (Wu, et al., 2019), ‘Machine Learning’, ‘Artificial Intelligence Algorithms’ into the ‘CardioHealth’ application

Email, face-to-face and telephonic conversation

Helen Mills

Head of the Database Group

Expertise in ‘Data Modelling’, ‘Programming’, ‘Data Entry’ and implement ‘Database Projects’ into the ‘CardioHealth’ application

Email, face-to-face and telephonic conversation

John Fuller

Head of the Cloud Computing Group

 Controls the data migration in the ‘CardioHealth’ project application’s cloud

Email, face-to-face and telephonic conversation

4.3 Appointment for team members (Emails) of the project on cardiovascular disease assignment

Dear Mr. Flynn,

We are very happy to appoint you to monitor the application development of ‘CardioHealth’ “ECG test” service provider. You will be holding the position of the Chief Information Officer under the “Information System Services” during the ‘CardioHealth’ project.

As we have discussed about the cause and implementation of the ‘CardioHealth’ as a cloud-based application in detecting and diagnosing “Cardiovascular Diseases” by ‘Machine Learning Algorithms’ technique. We will further discuss about your project remuneration and work structure briefly.

I will inform you the meeting venue and time in advance. For any further enquiry, send an email in this thread.

Sincerely,

IT Project Manager

CardioHealth

 

Dear Mr. Tilak

I am writing in behalf of the ‘CardioHealth’ project to inform you that you are appointed as the developer of the ‘CardioHealth’ business application. You will be holding the position of the Head of the Group in the Application Group.

We will discuss about the ‘CardioHealth’ application development to make it flexible for a large amount of data storage. We will further discuss about your project remuneration and work structure briefly.

I will inform you the meeting venue and time in advance. For any further enquiry, send an email in this thread.

Sincerely,

IT Project Manager

CardioHealth

 

Dear Mr. Henry,

We are congratulating you for your appointment as the Head of the Artificial Intelligence and Machine Learning Group. You will be monitoring the implementation of data mining, machine learning and AI algorithms in the ‘CardioHealth’ business application.

As we have discussed about the cause and implementation of the ‘CardioHealth’ as a cloud-based application in detecting and diagnosing “Cardiovascular Diseases” by ‘Machine Learning Algorithms’ technique. We will further discuss about your project remuneration and work structure briefly.

I will inform you the meeting venue and time in advance. For any further enquiry, send an email in this thread.

Sincerely,

IT Project Manager

CardioHealth

4.4 Appointments for project sponsor (Email)

Dear Mr. Davis,

We are very grateful to have you as the main investor of the ‘CardioHealth’ business application. We are also thankful to you for your application for a biomedical start-up grant from the government. You will be treated as a business partner in the ‘CardioHealth’ project.

As we have discussed about the cause and implementation of the ‘CardioHealth’ as a cloud-based application in detecting and diagnosing “Cardiovascular Diseases” by ‘Machine Learning Algorithms’ technique.

I will inform you the next meeting venue and time in advance.

Sincerely,

IT Project Manager

CardioHealth

5. Stakeholder Register

Name

Role

Requirements or Expectations

Interest

Power

Dr. Ben Williams and Prof. John Price

Project Initiator and Developer

Explaining the facilities and requirements in the development of the ‘CardioHealth’ business application

High

High

Arthur Davis

Project Investor

Fill the fund requirement during the application creation process and ensure the government biomedical start-up grant

High

High

Michael Flynn

Chief Information Officer

Monitoring the application creation process and inform about any error

High

High

Padma Tilak

Head of the Application Group

Developing the ‘CardioHealth’ application

High

Low

Bruce Henry

Head of the Artificial Intelligence and Machine Leaning Group

Implement AI based data and algorithms

High

Low

Helen Mills

Head of the Database Group

Data modelling, entry, programming and database specialist

High

Low

John Fuller

Head of the Cloud Computing Group

Supervise the data transformation on the cloud

High

Low

6. Project Carter

1. Project Overview

Name

‘CardioHealth’ business application

Investor

Arthur Davis

Manager

Duration

1st January 2020 – 31st December 2020

Project Purpose

Save the “ECG test” results on the ‘CardioHealth’ database cloud provided from ‘Machine Learning Algorithms’ technique

Budget /Resources

AUD 35,000

Arthur Davis and Government Biomedical Start-up Grant

Approved Date

10th April 2020

 

2. SCOPE

Purpose: It is noted herein cardiovascular disease assignment that documentation of “ECG test” results with ‘Machine Learning Algorithms’ technique to detect future heart diseases. The documented “ECG test” results can be assessible by the Cardiologists, general practitioners and the patients for further check-up and diagnosis.

Background: The “Cardiovascular Diseases” are one of the most common deadly disease in the present scenario. The ‘Machine Learning Algorithms’ technique analyse the “ECG test” report to predict heart diseases (Haq, et al., 2018). Studies considered in the cardiovascular disease assignment have shown that the ‘Machine Learning’ technique uses various methods for accurate and specific results.

Inclusion

Exclusion

· Patients with “Cardiovascular Diseases”

· Documentation of new and old “ECG test” results

· Analysing “ECG test” results in ‘Machine Learning’ technique by the Cardiologists directly from the cloud

· Giving feedback for appropriate treatment for heart diseases

· Diagnose process for “Cardiovascular Diseases”

 

3. Work Structure and Completion

Stages

Quantity

Start Date

Finish Date

Lead

Application framing, project development

Key features in the application, mechanism process, operation system, further development scopes

15th March 2020

9th April 2020

Dr. Ben Williams and Prof. John Price

Application development

Operation of the application, flexibility, detecting operation errors and its recovery

12th April 2020

8th October 2020

Padma Tilak

Artificial Intelligence and Machine Learning Algorithm

Data mining, machine learning, artificial intelligence algorithm implementation

2nd May 2020

10th October 2020

Bruce Henry

Database maintenance and cloud computing mechanism

Programming, data modelling and entry, data transfer system on the cloud

20th April 2020

25th October 2020

Helen Mills and John Fuller

 

4. Risk Factors associated with the project noted herein cardiovascular disease assignment

Category

Reason

Chances

Intensity

Application Flexibility

Running the application without any error, and providing enough RAM to save and run “ECG test” results smoothly

Medium

High

Upload Analysis results on time

Cardiologists will analyse the “ECG test” results and upload the ‘Machine Learning’ result file

Low

Medium

 

5. PROJECT BUDGET/ RESOURCES

Stage

Budget

Appointing an IT team in developing the application

AUD 5,000

Application Framework

AUD 1,080

Application Development

AUD 11,820

Database and Cloud Computing Development

AUD 10,550

Application Testing

AUD 6,550

Total

AUD 35,000

6. Work Breakdown Structure for the project explored in the cardiovascular disease assignment

cardiovascular disease assignment

7. Bibliography
Dhar, S. et al., 2018. A Hybrid Machine Learning Approach for Prediction of Heart Diseases. s.l., IEEE, pp. 1-6.

Fatima, M. & Pasha, M., 2017. Survey of machine learning algorithms for disease diagnostic. Cardiovascular disease assignment Journal of Intelligent Learning Systems and Applications, 9(01), p. 1.

Haq, A. U. et al., 2018. A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms. 2018(Special Issue), p. 21 pages.

Kreatsoulas, C. & Anand, S. S., 2010. The impact of social determinants on cardiovascular disease. [Online]
Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2949987/
[Accessed 12th March 2020].

Maji, S. & Arora, S., 2019. Decision tree algorithms for prediction of heart disease. In: Information and Communication Technology for Competitive Strategies. s.l.:Springer, pp. 447-454.

Mayo Clinic, 2018. Heart diseas- Diagnosis and treatment. [Online]
Available at: https://www.mayoclinic.org/diseases-conditions/heart-disease/diagnosis-treatment/drc-20353124
[Accessed 12th March 2020].

Nichenametla, R., Maneesha, T., Hafeez, S. & Krishna, H., 2018. Prediction of Heart Disease Using Machine Learning Algorithms. International Journal of Engineering and Technology(UAE), 31 5, Volume 7, pp. 363-366.

Rahman, A., Raka, S. C. & Ahmed, S. M., 2017. Prevalence of Cardiovascular Diseases and Prescription Patterns in a Randomly Selected Population in Bangladesh. Biomedical & Pharmacology Journal, 10(2), pp. 607-613.

Safdar, S., Zafar, S., Zafar, N. & Khan, N. F., 2018. Machine learning based decision support systems (DSS) for heart disease diagnosis: a review. Cardiovascular disease assignment Artificial Intelligence Review, 50(4), pp. 597-623.

Wu, C.-s. M., Badshah, M. & Bhagwat, V., 2019. Heart Disease Prediction Using Data Mining Techniques. s.l., s.n., pp. 7-11.

 

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