Machine learning

CSC8003 Machine Learning Assessment

28 March 2023 08:18 AM | UPDATED 1 year ago

CSC8003 Machine Learning Assessment :

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CSC8003  Machine learning Assessment
CSC8003 Machine learning Assessment


CSC8003 – Machine learning, S3 2022 Project specification

(Updated on 18 Jan 2023 according to the announcement made in relation to the correction of the due date of Report 4 at

Anaesthetic Data Analysis

Well Hospital is a fast-growing medical center in Toowoomba. In the past years, Well Hospital has served Toowoomba local community well. Aiming to increase the safety of surgery, Well Hospital is going to develop a Depth of Anaesthesia (DoA) index based on the features of raw electroencephalograph (EEG) data obtained from bispectral index (BIS) monitors. Against a group of talented programmers, you really want to win the contract to develop the system. But firstly you need to develop a new DoA index based on machine learning techniques and the data provided by Well Hospital.

What is DoA index?

It is important to assess the DoA accurately since a precise CSC8003 Machine learning Assessment assessment is helpful for avoiding various adverse reactions such as intraoperative awareness with recall (underdosage), prolonged recovery and an increased risk of postoperative complications for a patient (overdosage). Evidence shows that the depth of anaesthesia monitoring using electroencephalograph (EEG) improves patient treatment outcomes. For an accurate DoA assessment, intensive research has been conducted in finding “an ultimate index”, and various monitors and DoA algorithms were developed. The main process is presented in Figure 1. The BIS index (A-2000 BIS monitor; Aspect Medical Systems Inc., Newton, MA) is a single index derived from a set of time domain and frequency domain measures of EEG data. The BIS index is presented as a numerical index ranging from 100 (awake) to 0 (isoelectric EEG

/very deep anaesthetic state) and it is an important reference or benchmark for a newly developed DoA index.

Figure 1: New index design

Your objective in this project is to design a new index based on given parameter sets to assess the DoA of patients. The new index should be similar with BIS index (see Figure 2). The new index should also range from 100 (awake) to 0 (very deep anaesthetic state).

Figure 2: New index and BIS index

Data description

At the beginning of the project CSC8003 Machine learning Assessment , 15 cases of patients’ data are given by Well hospital as attachments. They include 10 cases training data (Train1 to Train10) and 5 cases testing data (Test1 to Test5). In each case of training data, there are 6 data sets which include one BIS index (BIS) and 5 parameter data sets (x1, x2, x3, x4 and x5). In each case of testing data, there are only 5 parameter data sets (x1, x2, x3, x4 and x5). The BIS data for testing data will be available after you submit your Model building report.

The BIS data is obtained from BIS monitor and the 5 parameter data sets are calculated from the raw EEG data using different feature extraction methods.

  • For each case, all the data sets (BIS, x1, x2, x3, x4 and x5) have the same number of data points.
  • All the data sets are stored chronologically. For examples, BIS(1) is the BIS value of the first second. x2(4) is the x2 parameter value calculated from the fourth second EEG data. Each parameter value is corresponding to its BIS value in the time series.
  • All the parameter data sets x1 from different cases are calculated by the same feature extracting methods. So do x2, x3, x4 and x5.

1.    Your task

In this project, you need to design a DoA index based on the training data set using the machine learning techniques you learnt in the CSC8003 course, and then assess the performance of your index based on the testing data set of CSC8003 Machine learning Assessment . During this project, you need to submit two following reports on different deadlines to achieve the 50% CSC8003 Machine learning Assessmentassessment.

  • Model building report, weight: 30%, due date: 7 February 2023.
    • Final project report, weight: 20%, due date: 24 February 2023.

2.    Model building report

In this report, the following contents should be covered:

  • Carry out research for machine learning applications on DoA assessment. Please summarize other researcher’s outcome and find out the limitations of different machine learning methods.
    • Analyse the data sets given in this project, what are their features?
    • According to the data sets and findings during your research, discuss which machine learning methods you will use in this project and show the reasons. At least two machine learning methods are discussed and used to do the parameter selections and index design.
    • According to findings during your research, discuss which DoA evaluation methods you will use to compare the new index with BIS index and show your reasons. For example, R square, Pearson coefficient and so on.
    • The parameter selection methods and results need to be present in your report clearly. You need to present your methods and results with key equations, figures or tables. The programming codes and supporting figures or excel data should be presented in the appendix of the report.
    • The DoA index design need to be presented in your report clearly and logically. You need to present your methods and results with key equations, figures or tables. The programming codes and supporting figures or Excel data should be presented in the appendix of the report. The BIS value cannot be any part of your new index. It means the new index is calculated by an equation including parameters (x1, x2, x3, x4, x5 or x6), not BIS value. The new index may be similar with (if you use the support vector machines or neutral networking, the model will be different):

New index = 4*x1+5*(x2)^2

If your new index design is just based on a simple liner regression without deep analysis, you cannot obtain a satisfied mark. If you use neural network or support vector machines, you may not provide the equation of new index as above. For different methods, the modeling presentations are different. Please present the feature selection, parameter setting or kernel function selection clearly.

  • The performance of new index is evaluated by comparing with BIS index based on training data sets. The results should be presented in tables or figures.
    • The Pearson coefficient is required to assess your results by marker in this project. Please show your Pearson coefficient results clearly in your report. In addition, you are also encouraged to use other methods to evaluate your results.
    • You can use the new index to assess the DoA of testing data sets. If the results are totally different with the training data sets. For examples, the new index value of testing data set are higher than 100 or lower than 0 in most time. You should consider revising your new index.
    • Discuss the problems you met and how you solved these problems in the process of DoA design. The content should be relevant to machine learning technique application, not time management or other management things.

3.    Final project report

In this report, the following contents should be covered:

  • The performance of new index is evaluated by comparing with BIS index based on testing data sets. The results are presented in tables or figures. The BIS data for testing data will be available on the StudyDesk after the deadline of index design report submission.
    • Deeply analysize the performance of your new index comparing with BIS index. Discuss in which aspects your new index performs well and in which aspects it is not.
    • Try to find the reasons and revise your DoA index. If it is hard to improve the index, you can do more research online about the machine learning methods you used to find out what other researchers mentioned the limitations of these methods. You need to write a discussion based on your findings.
    • Assume you obtained all the data from the website of Well Hospital through a temporary ID and password. Please discuss the risk or issue about the cyber security and ethic during the data transfer. Based on your analysis, you should also provide Well Hospital some advices how to solve these problems by machine learning techniques.
    • Write a summary to this project, including the outcome from the previous reports, difficulties you met during the whole project and your solutions, your deep understanding of machine learning techniques.

4.    Word limitation

  • The length of Part I – Model building report should be limited to 14 pages (excluding the title page, table of content, appendix and reference list) with not less than 1800 words.
  • The length of Part II – Final project report should be limited to 10 pages (excluding the title page, table of content, appendix and reference list) with not less than 600 words.

5.    Marking Criteria

The Marking Criteria are presented in another file:Project marking criteria. They include:

  • Model building report marking criteria
    • Final report marking criteria


  • The machine learning methods you used in this project should not be out of our course materials.
    • You are encouraged to use Python to do the programming code for this project. You can also use other software or programming language. However, you may not obtain advice or feedback from teaching staff for your code.
  • The experimental results are not the most important. The analysis and deep thinking about machine learning method applications are more significant. To obtain a full mark, you should present your finding and analysis clearly and logically.

6.    Plagiarism and Academic Misconduct

UniSQ has zero tolerance to academic misconduct including plagiarism and collusion. Plagiarism refers to the activities of presenting someone else’s work as if you wrote it yourself. Collusion is a specific type of cheating that occurs when two or more students exceed a permitted level of collaboration on a piece of assessment. Identical layout, identical mistakes, identical argument and identical presentation in students’ assignments are evidence of plagiarism and collusion. Such academic misconduct may lead to serious consequences, such as:

  • Required to undertake additional assessment in the course
    • Failed in the piece of assessment
    • Awarded a grade of Fail for the course
    • Withdrawn from the course with academic penalty
    • Excluded from the course or the program for a period of time


Refer to UniSQ Policy \Academic Misconduct” for further details.

7.    Submission

For each report, you should save all your documents in one word document. The assignment submission system will accept only the files with extensions specified (.doc/.docx/.odt). Please submit the different reports via different “Project report submission portal links” on the StudyDesk separately before their deadlines. You are allowed to submit only once. Make sure that the file you submit is the correct file and the correct version.


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