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SCHOOL OF COMPUTER SCIENCE

MASTER OF APPLIED COMPUTING (MAC)

ASSIGNMENT 3 (Weightage 30%)

SEPTEMBER 2024 SEMESTER

Assessment Criteria

MLO3 - Proposed and select suitable AI or Machine learning algorithm for a given application.

MLO4 - Analyze an AI-based solution for a given application.

C1 = Knowledge & Understanding, C2 = Cognitive Skills, C3A = Practical Skills, C3B = Interpersonal Skills, C3C = Communication Skills, C3D = Digital Skills, C3E = Numeracy Skills, C3F = Leadership, Autonomy & Responsibility, C4A = Personal Skills, C4B = Entrepreneurial Skills, C5 = Ethics & Professionalism

Scenario and Task Description

Predictive analytics in healthcare uses machine learning techniques and statistical algorithms to process real-time and historical patient data and discern patterns, trends, and associations for accurate predictions of individual health outcomes and population-wide events.

Itransition’s experts use their robust experience in predictive analytics software development and implementation to create custom solutions tailored specifically for healthcare providers in line with the latest industry trends and regulations.

The numerous benefits of predictive analytics for healthcare organizations, individual patients, and the population make it one of the most used types of analytics in the industry.

Predictive analytics helps healthcare professionals spot trends in patients’ medical records and genomic information that are impossible to detect manually, helping to understand patient conditions better. Predictive modeling, in particular, allows forecasting the course of the disease, enabling medical professionals to avoid health risks like adverse reactions to medicine, genetically determined resistance to treatment, and failure to adhere to the regimen. This way, clinicians receive actionable insights that help them optimize patients’ treatment plans according to their physical and psychological needs.

Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and humans through natural language. The goal of NLP is to enable machines to understand, interpret, and generate human language in a way that is both meaningful and useful. It is a multidisciplinary field, drawing from computer science, linguistics, and cognitive psychology. NLP can be used to as a predictive modelling in healthcare.

Practical Skills: Part I

1. Describe FOUR different techniques of NLP (Rule based, Statistical Methods, Machine Learning and Deep Learning). Ensure to relate your description with the usage of NLP. (2 marks)

2. Data cleaning step is crucial to ensure that you’re working with high-quality data. A good data scientist will spend around 70-90% of their time cleaning their data. Use Pandas and NumPy libraries to perform some of the required data cleaning steps. (you can include data normalization-encoding and scaling if necessary) (5 marks)

3. Carrying out a simple Exploratory Data Analysis (EDA) from the healthcare.csv dataset. Predicted column has three values (Normal," "Abnormal," or "Inconclusive”). Perform a way to make the predicted column into only a binary classification. (3 marks)

4. When your data is ready for modelling, you can start building your prediction model. Perform TWO (2) modelling (please use only the modelling covered in the module). Justify the selected Machine Learning models and describe them. (5 marks)

Practical Skills: Part II

5. There are TWO (2) basic matrices to measure a performance from a Machine Learning model. Describe those TWO (2) matrices. (4 marks)

6. Evaluate the performance for both Machine Learning models you used in Part I. Show their accuracy, precision and recall values. Explain the results you have received for both models.

(8 marks)

7. Prepare a sample of test data and show how the best model you built can predict whether the test data can give positive or negative emotion. (3 marks)

Submission Requirements

1. Font type : Times New Roman

2. Font size : 12

3. Line spacing : 1.5

4. Alignment : Justify Text

5. Document type : .pdf, .ipynb

6. Number of pages : 5 – 12 pages (do not exceed the page limit)

7. Your full report should consist of the following:

a) Cover page (Name, ID, Date, Signature, Score)

b) Marking Rubrics & Declaration (attach as second page in the report)

c) Report of your answer script

d) Appendixes (line spacing = 1.0)

· List of references (APA format)

· Python script

· Report of similarity score (percentage of similarity score from each source needs to be shown)

8. Start each question on a separate page.

9. All figures and tables are labelled properly.

10. File naming conventions: StudentName_Assignment1

Notes:

· Student is not allowed to transcribe directly (copy and paste) any material from another source into their submission.

· Start each question on a new page.

· Answer in form of short essay (50 to 200 words) and print out the relevant Python program outputs

· All process/functions must be clearly explained.

· Include in-text citation to support your answers and add the list of references at the end of your report (APA format). The list of references is to be alphabetized by the first author's last name, or (if no author is listed) the organization or title.

· The Turnitin similarity for this module is 20% overall and lesser than 1% from a single source excluding program source codes.