Machine Learning Assignment: Using Supervised Machine Learning to Predict In-Hospital Mortality

Machine Learning Assignment:
Using Supervised Machine Learning
to Predict In-Hospital Mortality
Aim
To apply learned data exploration and machine learning skills to
design and implement an end-to-end machine learning pipeline
for predicting a binary outcomes from a highly dimensional realworld dataset.
Indicative Timetable
The following table shows a breakdown of the activities for this
week to help you in prioritising your time schedule. The actual
time needed to complete each task is dependent on your
implementation/analysis speed and personal circumstances. The
timetable provided below is merely representative of the average
time needed to complete each task and the time needed for each
relative to other tasks. The timetable is also provided to ensure
that you know that the assignment is time-consuming and
requires thoughtful planning to complete all required tasks.
Activity Tentative Duration
Data Cleaning 2 Days
Data Aggregation 3-4 Days
Data Exploration & Visualisation 2 Day
Classifier Implementation &
Hyperparameter Tuning
4-5 Days
Model Evaluation 2 Days
Analysis and Reflection 2 Days
Writing 3 Days
Assignment Details
Now that you have been equipped with the skills to use different
Machine Learning algorithms, you will have the opportunity to practice
and apply it to a practical problem using real-world hospital data
extracted from the MIMIC-III database. The dataset consists of
measurements of 25 laboratory test results and vital signs for 2670
patients recorded over 48 hours.
In this assignment, you will complete and submit a Jupyter notebook
containing an end-to-end supervised learning pipeline using the XGBoost
algorithm to predict 30-day in-hospital mortality from aggregates of
patient vital signs and laboratory test results given in the
PneumoniaTimeSeries.csv dataset.
Recap of Supervised Learning
The majority of practical machine learning uses supervised learning. In
supervised learning, an algorithm learns a function that maps input
variables (X) to an output variable (y), i.e. y = f(X). The goal is to
approximate the real underlying mapping so that when the algorithm is
supplied with unseen data, it can predict the output variables (y) for the
new samples. Supervised learning is called so because the process of an
algorithm learning from the training dataset can be thought of as a
teacher supervising the learning process.
During supervised training, we know the correct answers; the algorithm
iteratively makes predictions on the training data and is corrected by
making updates. Learning stops when the algorithm achieves an
acceptable level of performance.
Supervised learning problems can be further grouped into regression
and classification.
• Classification: A classification problem is when the output variable
is a category, such as “red” and “blue” or “disease” and “no
disease.”
• Regression: A regression problem is when the output variable is a
real value, such as “dollars” or “weight.”
Description of the Fields
The PatientID field contains the patient identifiers. The outcome field
(Mortality30Days) contains the outcome for each patient. The fields in
each record are:
Field name Description
PatientID Unique Patient identifier