[2022] MCD4710 Introduction to Algorithms and Programming Assignment 1
MCD4710 - Introduction to Algorithms and Programming Assignment 1 (8%)
Due: Thursday, July 28, 2022, 11:55 pm - Week 6
Marking Criteria
This assignment contributes to 8% of your final mark. Task 1 – Read data: 3 marks
Task 2 – Convert weight to kg: 2 marks
Task 3 – Calculate BMI: 2 marks
Task 4 – Categorise patients: 3 marks
Task 5 – Get BMI: 2 marks
Task 6 – Get blood pressure: 2 marks
Task 7 – Check smoking status: 2 marks Task 8 – Extract high-risk records: 4 marks Programming practice – 2 marks
● Decomposition (1 mark)
● Variable names and code Documentation (1 mark) Total: 22 marks
Background Information
A data set containing patient information from a hospital (a modified version of a data set taken from MathWorks sample data sets: hospital.mat) is provided in a CSV file (hospital_data.csv) and contains records of 100 patients with the 8 fields; name, gender, age, weight (lbs), height (cm), smoking status, systolic blood pressure and diastolic blood pressure.
You are required to write functions to achieve the following (note that this dataset is just for the purposes of testing, and your module must work for any similar dataset as well).
Task 1: Read the data into a table
Implement a function named read_data(filename), with the following specification, which reads a given file and returns the contents in a table. Make sure you store the smoking status (in the 6th column) as an integer and the other numerical values as floats.
Hint: you can use the isalpha(string) string method (you may assume none of the fields in the data file are alphanumeric).
Input: The name of the data file (filename) Output: A table with data in the given file.
For example:
>>> patient_data = read_data(‘hospital_data.csv’)
>>> patient_data[:3] # The first three rows of the table
[['SMITH', 'M', 38.0, 176.0, 145.0, 1, 124.0, 93.0], ['JOHNSON', 'M', 43.0, 163.0, 146.0, 0, 109.0, 77.0], ['WILLIAMS', 'F', 38.0, 131.0, 147.0, 0, 125.0, 83.0]]
Task 2: Convert the weight to kilograms
The weight of each patient is given in pounds (lbs). Write a function named lbs_to_kg(table), which converts the weight to kilograms (kg) and replaces the original weight in the table, for each patient. Make sure the weight in kg is rounded to one decimal place.
Note: 1 lbs = 0.4536 kg
Input: A table containing all the patient data that is available in the given data file. Output: The table with weight converted to kilograms
For example:
>>> patient_data = read_data('hospital_data.csv')
>>> lbs_to_kg(patient_data)
>>> patient_data[:3] # The first three rows of the modified table
[['SMITH', 'M', 38.0, 79.8, 145.0, 1, 124.0, 93.0], ['JOHNSON', 'M', 43.0, 73.9, 146.0, 0, 109.0, 77.0], ['WILLIAMS', 'F', 38.0, 59.4, 147.0, 0, 125.0, 83.0]]
Task 3: Calculate the body mass index (BMI)
The body mass index (BMI) for each patient needs to be calculated. Write a function named calculate_BMI(table), which calculates the BMI for each person, using the formula below. Append the BMI values, rounded to one decimal place, as the last column of the table.
mass (kg) height2 (m2 )
Input: A table containing all the patient data that is available in the given data file, with the weight converted to kg.
Output: The table with the BMI values added to the last column of the table.
For example:
>>> patient_data = read_data('hospital_data.csv')
>>> lbs_to_kg(patient_data)
>>> calculate_BMI(patient_data)
>>> patient_data[:3] # The first three rows of the modified table
[['SMITH', 'M', 38.0, 79.8, 145.0, 1, 124.0, 93.0, 38.0], ['JOHNSON', 'M', 43.0, 73.9, 146.0, 0, 109.0, 77.0, 34.7], ['WILLIAMS', 'F', 38.0, 59.4, 147.0, 0, 125.0, 83.0, 27.5]]
Task 4: Categorize based on BMI
The patients in the data table need to be categorized according to their BMI value. Write a function named categorise(table) that determines the category for each person based on the following classification and store characters U, N or O for underweight, normal and obese, respectively. This information should be stored next to their BMI value in the table. The categorization should be done according to the following classification.
BMI <18:Underweight 18 £ BMI < 30 : Normal
BMI ³ 30 : Obese
Input: A table containing all the patient data that is available in the given data file, with the BMI values appended.
Output: The table with the categories added to the last column of the table.
BMI=
For example:
>>> patient_data = read_data('hospital_data.csv') >>> lbs_to_kg(patient_data)
>>> calculate_BMI(patient_data)
>>> categorise(patient_data)
>>> patient_data[:3] # The first three rows of the modified table
[['SMITH', 'M', 38.0, 79.8, 145.0, 1, 124.0, 93.0, 38.0, ‘O’], ['JOHNSON', 'M', 43.0, 73.9, 146.0, 0, 109.0, 77.0, 34.7, ‘O’], ['WILLIAMS', 'F', 38.0, 59.4, 147.0, 0, 125.0, 83.0, 27.5, ‘N’]]
Task 5: Look up BMI value of a given patient
Write a function named get_BMI(table,name) that returns the BMI value of a given patient by looking up the name in the table. If the given name is not available in the table, the function should return None. You may assume that the names are unique.
Input: A table containing all the patient data that is available in the given data file, with the BMI values appended and the name of a patient.
Output: The BMI value of the given patient.
For example:
>>> patient_data = read_data('hospital_data.csv') >>> lbs_to_kg(patient_data)
>>> calculate_BMI(patient_data)
>>> get_BMI(patient_data,’Johnson’)
34.7
Task 6: Look up blood pressure value of a given patient
Write a function named get_BP(table,name) that returns the blood pressure readings as a tuple, (systolic, diastolic), of a given patient by looking it up in the table. Make sure the blood pressure readings are returned as integer values. If the given name is not available in the table, the function should return None. You may assume that the names are unique.
Input: A table containing all the patient data that is available in the given data file and the name of a patient.
Output: The blood pressure readings of the given patient as a tuple.
For example:
>>> patient_data = read_data('hospital_data.csv') >>> get_BP(patient_data,’Johnson’)
(109, 77)
Task 7: Determine whether a given patient is a smoker or not
Write a function named is_smoker(table,name) that returns True if the given patient is a smoker and False if not. The function should return None if the patient record is not available in the table.
Input: A table containing all the patient data that is available in the given data file and the name of a patient.
Output: True if the patient is a smoker and False otherwise.
For example:
>>> patient_data = read_data('hospital_data.csv') >>> is_smoker(patient_data,’Johnson’)
False
>>> is_smoker(patient_data,’White’)
True
Task 8: Extract the records of high-risk patients and write to a separate csv file
Now we need to extract the records of high-risk patients from the table and save their details (all column values of the patient in the table) into a new csv file. Write a function named extract_high_risk(table,filename) that extracts the records of patients who satisfy all of the following conditions and write their data into the given file, output_filename. Add the column headings to the file as well.
A patient is deemed at high-risk if they
- ● are obese
- ● has either systolic blood pressure greater than 125 or diastolic blood pressure greater than 85.
- ● are a smoker
Note: You have to use the functions written in the previous tasks. Do not rewrite the code written in the previous tasks here.
Input: A table containing all the patient data and a filename Output: The given file updated with the high-risk patient details
For example:
>>> patient_data = read_data('hospital_data.csv') >>> lbs_to_kg(patient_data)
>>> calculate_BMI(patient_data)
>>> categorise(patient_data)
>>> extract_high_risk(patient_data,'high_risk_patients.csv')
>>> high_risk_table = read_data('high_risk_patients.csv') # read high-risk values (for testing)
high-risk records [['SMITH', 'M', 38.0, 79.8, 145.0, 1, 124.0, 93.0, 38.0, 'O'], ['WHITE', 'M', 39.0, 91.6, 152.0, 1, 130.0, 95.0, 39.6, 'O']]
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