I only require 4th qus to be solved
INFO5306 Enterprise Healthcare Information Systems Assignment 2 (Individual) – Health Data Analysis 20 Marks in Total Weight: 10% of overall UoS Due Date: Report (Week 13 - Wed 6th November 5pm) Objective This assessment is designed to let you demonstrate your understanding of the data analysis software you used during the lab sessions, such as Microsoft Excel Pivot. You are required to prepare a report that answers several questions specified in this document. Data Sets and Software Availability: • All the software you need to do this assignment are available on the Lab PCs. You can do the assignment using your own PC but it is your responsibility to get the same answer as the Lab PC. • For Q1, the recommended tool is Microsoft Excel Pivot Table. The required dataset is: o Medicare_Provider_Charge_Outpatient_APC30_CY2013.csv and has 44,356 rows. • For Q2, the recommended tool is Microsoft Excel Pivot Table. The required datasets are: o Medicare_Provider_Charge_Outpatient_APC30_CY2013.csv o Medicare_Provider_Charge_Outpatient_APC30_CY2012.csv o Medicare_Provider_Charge_Outpatient_APC30_CY2011.csv • For Q3, the recommended tool is MATLAB. The required dataset is: o Diagnostic_Breast_Cancer.xlsx and has 461 rows. • For Q4, the recommended tool is MATLAB. The required dataset is “SkinLesionDataset”. It has a train folder (5,400 images) and a val folder (12 images). More details given in Q4. Warning: The data files are strictly used for the INFO5306 assignment only. You are not allowed to copy, distribute, adapt, display or include the data in any situations outside this class. The data maybe inaccurate or misleading when used in real life. 2 Semester 2, 2019 Submission and Notes: • You must use the provided answer sheet “info5306-assign2-answer-sheet.docx”. • You need to submit an electronic copy using Canvas. • For the electronic copy, you must submit your answers using your Canvas account. You may submit as many times as you like until the due date. You need to submit two files to Canvas: o One answer sheet file that includes all your answers. ▪ You need to rename it to “info5306-ass2-STUDENTID”, where STUDENTID is your student ID. ▪ Save your submission as a PDF. o One zip file containing all your workspace files for question 1 to 4, for example Q1.xlsx files for Question 1. ▪ Your workspace files will be used to check how you arrived at your answer. ▪ Matlab workspace can be saved using “save(filename)” command. More details can be found here: https://au.mathworks.com/help/matlab/ref/save.html ▪ Name the zip file “info5306-assign2- STUDENTID.zip” • You can access MATLAB on their personally owned PC while they are enrolled at the University. For more information see http://sydney.edu.au/students/student-it/apps.html. • Each question indicates the tool that is recommended that you use to develop your answer. However, you may use other tools. You must check with the lecturers first and also indicate the tool used in your assignment answer. • The tutorial materials covered the approach to the assignment questions. However, some of the questions may require additional study. TIP: Read the questions carefully. https://au.mathworks.com/help/matlab/ref/save.html https://au.mathworks.com/help/matlab/ref/save.html http://sydney.edu.au/students/student-it/apps.html http://sydney.edu.au/students/student-it/apps.html 3 Semester 2, 2019 Q1. [5 marks in total] (Recommended Tool: Excel) The dataset includes estimated hospital-specific charges for 30 Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Years (CY) 2013. For these APCs, the estimated average charges and the average Medicare payments are provided at the individual hospital level. APC Code and description identifying the Ambulatory Payment Classification. APCs are a classification system where individual services (Healthcare Common Procedure Coding System [HCPCS] codes) are assigned based on similar clinical characteristics and similar costs. Provider ID The provider identifier assigned to the Medicare certified hospital facility. Provider Name The name of the provider. Provider Street Address The provider’s street address. Provider City The city where the provider is located. Provider State The state where the provider is located. Provider Zip Code The provider’s zip code. Hospital Referral Region (HRR) Description The Hospital Referral Region (HRR) where the provider is located. Outpatient Services The number of services billed by the provider for outpatient hospital services. Average Estimated Submitted Charges The provider's average estimated submitted charge for services covered by Medicare for the APC. These will vary from hospital to hospital because of differences in hospital charge structures. Average Total Payments The average of total payments to the provider for the APC including the Medicare APC amount. Also included in Total Payments are co-payment and deductible amounts that the patient is responsible for. You may give your answers to two decimal places. Note that in the template tables provided, the exact wordings of the headings may be different. Q1.1. [1 mark] Calculate the top five providers that received the highest average payments for their services. Sort the values from high to low. Your table should look like the one below. Provider Payment 1 2 3 4 5 Grand Total 4 Semester 2, 2019 Q1.2. [1 mark] Which two types of APC had the lowest average in the Average Estimated Submitted Charges in states CA, NY, and TX? What were the charges. Your table should look like the one below. APC … Estimated Submitted Charges 1 2 Grand Total Q1.3. [1 mark] You will need to add a new data field Average Ratio to answer this question. The Average Ratio is defined as (Average Total Payments/Average Estimated Submitted Charges). Find out which APCs have the highest maximum and lowest minimum Average Ratio. Your table should look like the one below. APC Highest Maximum 1 Grand Total Q1.4 [2 marks] Use PivotTable to create one histogram table (bin size of 5000, minimum = 0 and maximum = 40,000) to demonstrate the number of Outpatient Services in Average Estimated Charge for each Region. The answer should look like the following Midwest Region Bin size = 5000 Number of Outpatient Services 0 – 4999 5000 – 9999 … … … Northeast Region Bin size = 5000 Number of Outpatient Services 0 – 4999 5000 – 9999 … … … … … 5 Semester 2, 2019 Q2. [5 marks total] (Recommended Tool: Excel) You are working as an analyst and you are requested to generate a short report to analyse changing patterns in the data of the US Medicare Outpatient Prospective Payment System. You are asked to look at the data from a spatial and temporal perspective, and to analyse changes from Year 2011 to 2013. Your task: • Analyse the data, and identify a pattern. • Create report that: o Summarises the changes in the selected pattern over time. [1 mark] o Quantifies whether the pattern changes differently at different locations. [1 mark] o Includes at least one informative chart or graph. [1 mark] o Makes a recommendation about future strategy based on this data. [1 mark] o Is written in a clear style and format. [1 mark] o MAXIMUM of two pages long. HINTS: ▪ You should analyse the data and use it to justify your findings ▪ You may need to create new fields ▪ You may need to learn new techniques or functions by yourself 6 Semester 2, 2019 Q3. [5 marks total] (Recommended Tool: MATLAB) You are given a breast cancer dataset. It contains the correct diagnosis made by physicians (ground truth) as well as by four different algorithms. The attributes of the data are as follows: • Diagnosis: o 1 = malignant o 0 = benign • Algorithm_A, Algorithm_B, Algorithm_C and Algorithm_D: o 1 = predicted malignant o 0 = predicted benign Q3.1. [1 mark] Calculate the number of true positives (TP), false positives (FP), true negatives (TN) and false negatives (FN) for each algorithm. Algorithm A Algorithm B Algorithm C Algorithm D TP FP TN FN Q3.2. [1 mark] Calculate the Sensitivity, Specificity, Precision, Recall, F-score and Accuracy for these four algorithms. Algorithm A Algorithm B Algorithm C Algorithm D Sensitivity Specificity Precision Recall F-score Accuracy Q3.3. [1 mark] Create a Receiver Operating Characteristics curve for each of the four algorithms. Q3.4. [2 marks] You have been asked to make a recommendation about the algorithm that should be used in an automated decision support system. Which of the four algorithms would you recommend? Justify your answer. 7 Semester 2, 2019 Q4. [5 marks total] (Recommended Tool: MATLAB) Skin cancer is the most common cancer in Australia, with melanoma being the most deadly form. Dermoscopy is a skin imaging modality that has demonstrated improvement for diagnosis of skin cancer compared to unaided visual inspection. However, even for experienced dermatologists, diagnosis by human vision can be subjective, inaccurate and non-reproducible. This is attributed to the complexity of lesion due to variations in size and shape, fuzzy lesion boundaries and different skin colour types. Motivated by this difficulty, you are required to develop a computer-aided diagnosis (CAD) systems that can assist the dermatologists’ clinical diagnosis. Dataset: You are given a SkinLesionDataset. It has a train folder (5,400 images) and a val folder (12 images). Data structure is like the followings: Your Task: The goal for this CAD is to classify dermoscopic images among 8 different diagnostic categories (AK, BCC, BKL, DF, MEL, NV, SCC and VASC). You are required to build a simple convolutional neural networks (CNN) based on the train folder. The trained CNN model will be used to predict the clinical diagnosis of the images in the val folder. The answer