Machine learning with Python, need code , and write report to analysis algorithms - Statistics
A Similarity score MUST NOT exceed 30% in any case and code.
Requirements: 2000 - 3500 words with code
heart_failure_clinical_records_dataset.csv
age anaemia creatinine_phosphokinase diabetes ejection_fraction high_blood_pressure platelets serum_creatinine serum_sodium sex smoking time DEATH_EVENT
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__MACOSX/._heart_failure_clinical_records_dataset.csv
Background
In this project you are given a dataset and an article that uses this dataset. The
authors have developed ten ML models for predicting survival of patients with heart
failure and compared their performance. You must read the article to understand the
problem, the dataset, and the methodology to complete the following tasks.
Dataset
The dataset contains the medical records of patients who had heart failure, collected
during their follow-up period. Each patient profile has 13 clinical features. A detailed
description of the dataset can be found in the Dataset section of the provided article
(patient_survival_prediction.pdf).
Tasks:
1. Read the article and reproduce the results presented in Table-4 using Python
modules and packages (including your own script or customised codes). Write a report
summarising the dataset, used ML methods, experiment protocol and results including
variations, if any. During reproducing the results:
i) you should use the same set of features used by the authors.
ii) you should use the same classifier with exact parameter values.
iii) you should use the same training/test splitting approach as used by the authors.
iv) you should use the same pre/post processing, if any, used by the authors.
v) you should report the same performance metrics as shown in Table-4.
N.B.
(i) Some of the ML methods are not covered in the current unit. Consider them as HD
tasks i.e., based on the knowledge gained in the unit you should be able to find
necessary packages and modules to reproduce the results.
(ii) If you find any issue in reproducing results or some subtle variations are found due
to implementation differences of packages and modules in Python then appropriate
explanation of them will be considered during evaluation of your submission.
(iii) Similarly, variation in results due to randomness of data splitting will also be
considered during evaluation based on your explanation.
(iii) Obtained marks will be proportional to the number of ML methods that you will
report in your submission with correctly reproduced results.
(iv) Make sure your Python code segment generates the reported results, otherwise
you will receive zero marks for this task.
Criteria:
appropriately implemented >=90% of the methods presented in the article. Variation of
marks in this group will depend on the quality of report.
2. Design and develop your own ML solution for this problem. The proposed solution
should be different from all approaches mentioned in the provided article. This does
not mean that you must have to choose a new ML algorithm. You can develop a
novel solution by changing the feature selection approach or parameter optimisations
process of used ML methods or using different ML methods or different combinations
of them. This means, the proposed system should be substantially different from the
methods presented in the article but not limited to only change of ML methods.
Compare the result with reported methods in the article. Write a technical report
summarising your solution design and outcomes. The report should include:
i) Motivation behind the proposed solution.
ii) How the proposed solution is different from existing ones.
iii) Detail description of the model including all parameters so that any reader can
implement your model.
iv) Description of experimental protocol.
v) Evaluation metrics.
vi) Present results using tables and graphs.
vii) Compare and discuss results with respect to existing literatures.
viii) Appropriate references (IEEE numbered).
Criteria:
an appropriate solution presented whose performance is better than the best
reported performances in the article (Table 11). The variation in the marking in this
group will depend on the quality of the report.
3. Present your result in a 3 minutes video using PowerPoint slides/animation.
Chicco and Jurman BMC Medical Informatics and Decision Making (2020) 20:16
https://doi.org/10.1186/s12911-020-1023-5
RESEARCH ARTICLE Open Access
Machine learning can predict survival of
patients with heart failure from serum
creatinine and ejection fraction alone
Davide Chicco1* and Giuseppe Jurman2
Abstract
Background: Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly
exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough
blood to meet the needs of the body.
Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values,
which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise
undetectable by medical doctors. Machine learning, in particular, can predict patients’ survival from their data and can
individuate the most important features among those included in their medical records.
Methods: In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several
machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most
important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics
tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking
approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the
machine learning survival prediction models on these two factors alone.
Results: Our results of these two-feature models show not only that serum creatinine and ejection fraction are
sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone
can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an
analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are
the most predictive clinical features of the dataset, and are sufficient to predict patients’ survival.
Conclusions: This discovery has the potential to impact on clinical practice, becoming a new supporting tool for
physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at
understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.
Keywords: Cardiovascular heart diseases, Heart failure, Serum creatinine, Ejection fraction, Medical records, Feature
ranking, Feature selection, Biostatistics, Machine learning, Data mining, Biomedical informatics
Background
Cardiovascular diseases (CVDs) are disorders of the heart
and blood vessels including, coronary heart disease (heart
attacks), cerebrovascular diseases (strokes), heart failure
(HF), and other types of pathology [1]. Altogether, car-
diovascular diseases cause the death of approximately 17
*Correspondence: [email protected]
1Krembil Research Institute, Toronto, Ontario, Canada
Full list of author information is available at the end of the article
million people worldwide annually, with fatalities figures
on the rise for first time in 50 years the United Kingdom
[2]. In particular, heart failure occurs when the heart is
unable to pump enough blood to the body, and it is usu-
ally caused by diabetes, high blood pressure, or other heart
conditions or diseases [3].
The clinical community groups heart failure into two
types based on the ejection fraction value, that is the pro-
portion of blood pumped out of the heart during a single
© The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
http://crossmark.crossref.org/dialog/?doi=10.1186/s12911-020-1023-5&domain=pdf
http://orcid.org/0000-0001-9655-7142
http://orcid.org/0000-0002-2705-5728
mailto: [email protected]
http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/publicdomain/zero/1.0/
Chicco and Jurman BMC Medical Informatics and Decision Making (2020) 20:16 Page 2 of 16
contraction, given as a percentage with physiological val-
ues ranging between 50% and 75%. The former is heart
failure due to reduced ejection fraction (HFrEF), previ-
ously known as heart failure due to left ventricular (LV)
systolic dysfunction or systolic heart failure and char-
acterized by an ejection fraction smaller than 40% [4].
The latter is heart failure with preserved ejection frac-
tion (HFpEF), formerly called diastolic heart failure or
heart failure with normal ejection fraction. In this case, the
left ventricle contracts normally during systole, but the
ventricle is stiff and fails to relax normally during diastole,
thus impairing filling [5–10].
For the quantitative evaluation of the disease pro-
gression, clinicians rely on the New York Heart Asso-
ciation (NYHA) functional classification, including four
classes ranging from no symptoms from ordinary activi-
ties (Class I) to a stage where any physical activity brings
on discomfort and symptoms occur at rest (Class IV).
Despite its widespread use, there is no consistent method
of assessing the NYHA score, and this classification fails
to reliably predict basic features, such as walking distance
or exercise tolerance on formal testing [11].
Given the importance of a vital organ such as the heart,
predicting heart failure has become a priority for medi-
cal doctors and physicians, but to date forecasting heart
failure-related events in clinical practice usually has failed
to reach high accuracy [12].
In this context, electronic health records (EHRs, also
called medical records) can be considered a useful
resource of information to unveil hidden and non-obvious
correlations and relationships between patients’ data, not
only for research but also for clinical practice [13, 14] and
for debunking traditional myths on risk factors [15, 16]. To
this aim, several screening studies have been conducted
in the last years, covering different conditions and demo-
graphics and with different data sources, to deepen the
knowledge on the risk factors. Among them, it is worth
mentioning the PLIC study [17], where EHRs, blood test,
single-nucleotide polymorphisms (SNPs), carotid ultra-
sound imaging, and metagenomics data have been col-
lected in a four-visit longitudinal screening throughout 15
years in Milan (Italy, EU) to support a better assessment
of cardiovascular disease risk.
Machine learning applied to medical records, in partic-
ular, can be an effective tool both to predict the survival
of each patient having heart failure symptoms [18, 19],
and to detect the most important clinical features (or risk
factors) that may lead to heart failure [20, 21]. Scien-
tists can take advantage of machine learning not only for
clinical prediction [22, 23], but also for feature ranking
[24]. Computational intelligence, especially, shows its pre-
dictive power when applied to medical records [25, 26],
or coupled with imaging [27–29]. Further, deep learning
and meta-analysis studies applied to this field have also
recently appeared in the literature [30–33], improving on
human specialists’ performance [34], albeit showing lower
accuracy (0.75 versus 0.59).
Modeling survival for heart failure (and CVDs in gen-
eral) is still a problem nowadays, both in terms of achiev-
ing high prediction accuracy and identifying the driving
factors. Most of the models developed for this purpose
reach only modest accuracy [35], with limited inter-
pretability from the predicting variables [36]. More recent
models show improvements, especially if the survival out-
come is coupled with additional targets (for example,
hospitalization [37]). Although scientists have identified a
broad set of predictors and indicators, there is no shared
consensus on their relative impact on survival prediction
[38]. As pointed out by Sakamoto and colleagues [39], this
situation is largely due to a lack of reproducibility, which
prevents drawing definitive conclusions about the impor-
tance of the detected factors. Further, this lack of repro-
ducibility strongly affects model performances: general-
ization to external validation datasets is often inconsistent
and achieves only modest discrimination. Consequently,
risk scores distilled from the models suffer similar prob-
lems, limiting their reliability [40]. Such uncertainty has
led to the proliferation of new risk scores appearing in
the literature in the last years, with mixed results [41–47].
As a partial solution to improve models’ effectiveness,
recent published studies included cohorts restricted to
specific classes of patients (for example, elderly or dia-
betic) [48, 49]. These attempts have led to tailored models
and risk scores [50, 51] with better but still not optimal
performance.
In this paper, we analyze a dataset of medical records
of patients having heart failure released by Ahmad and
colleagues [52] in July 2017. Ahmad and colleagues [52]
employed traditional biostatistics time-dependent mod-
els (such as Cox regression [53] and Kaplan–Meier sur-
vival plots [54]) to predict mortality and identify the key
features of 299 Pakistan patients having heart failure,
from their medical records. Together with their analy-
sis description and results, Ahmad and coworkers made
their dataset publicly available online (“Dataset” section),
making it freely accessible to the scientific community
[55]. Afterwards, Zahid and colleagues [56] analyzed the
same dataset to elaborate two different sex-based mortal-
ity prediction models: one for men and one for women.
Although the two aforementioned studies [52, 56] pre-
sented interesting results, they tackled the problem by
standard biostatistics methods, leaving room for machine
learning approaches. We aim here to fill this gap by using
several data mining techniques first to predict survival of
the patients, and then to rank the most important features
included in the medical records. As major result, we show
that the top predictive performances can be reached by
machine learning methods with just two features, none
Chicco and Jurman BMC Medical Informatics and Decision Making (2020) 20:16 Page 3 of 16
of them coming unexpected: one is ejection fraction, and
the other is serum creatinine, well known in the literature
as a major driver of heart failure [57–62], and also a key
biomarker in renal dysfunction [63–65].
In particular, we first describe the analyzed dataset
and its features (“Dataset” section), and then the
methods we employed for survival prediction and
feature ranking (“Methods” section). In the Results
section (“Results” section), we report the survival pre-
diction performances obtained through all the employed
classifiers (“Survival machine learning prediction on all
clinical features” section), the ranking of the features
obtained through traditional biostatistics techniques and
machine learning (“Feature ranking results” section), and
the survival prediction performances achieved by employ-
ing only the top two features identified through fea-
ture ranking (ejection fraction and serum creatinine,
“Survival machine learning prediction on serum creati-
nine and ejection fraction alone” section). Later, we report
and describe the results of the analysis that includes the
patients’ follow-up time (Table 11). Finally, we discuss the
results (“Discussion” section) and draw some conclusions
at the end of the manuscript (“Conclusions” section).
Dataset
We analyzed a dataset containing the medical records of
299 heart failure patients collected at the Faisalabad Insti-
tute of Cardiology and at the Allied Hospital in Faisalabad
(Punjab, Pakistan), during April–December 2015 [52, 66].
The patients consisted of 105 women and 194 men, and
their ages range between 40 and 95 years old (Table 1). All
299 patients had left ventricular systolic dysfunction and
had previous heart failures that put them in classes III or
IV of New York Heart Association (NYHA) classification
of the stages of heart failure [67].
The dataset contains 13 features, which report clinical,
body, and lifestyle information (Table 1), that we briefly
describe here. Some features are binary: anaemia, high
blood pressure, diabetes, sex, and smoking (Table 1). The
hospital physician considered a patient having anaemia
if haematocrit levels were lower than 36% [52]. Unfortu-
nately, the original dataset manuscript provides no defini-
tion of high blood pressure [52].
Regarding the features, the creatinine phosphokinase
(CPK) states the level of the CPK enzyme in blood. When
a muscle tissue gets damaged, CPK flows into the blood.
Therefore, high levels of CPK in the blood of a patient
might indicate a heart failure or injury [68]. The ejec-
tion fraction states the percentage of how much blood
the left ventricle pumps out with each contraction. The
serum creatinine is a waste product generated by cre-
atine, when a muscle breaks down. Especially, doctors
focus on serum creatinine in blood to check kidney func-
tion. If a patient has high levels of serum creatinine, it
may indicate renal dysfunction [69]. Sodium is a min-
eral that serves for the correct functioning of muscles and
nerves. The serum sodium test is a routine blood exam
that indicates if a patient has normal levels of sodium in
the blood. An abnormally low level of sodium in the blood
might be caused by heart failure [70]. The death event
feature, that we use as the target in our binary classifica-
tion study, states if the patient died or survived before the
Table 1 Meanings, measurement units, and intervals of each feature of the dataset
Feature Explanation Measurement Range
Age Age of the patient Years [40, ..., 95]
Anaemia Decrease of red blood cells or hemoglobin Boolean 0, 1
High blood pressure If a patient has hypertension Boolean 0, 1
Creatinine phosphokinase Level of the CPK enzyme in the blood mcg/L [23, ..., 7861]
(CPK)
Diabetes If the patient has diabetes Boolean 0, 1
Ejection fraction Percentage of blood leaving Percentage [14, ..., 80]
the heart at each contraction
Sex Woman or man Binary 0, 1
Platelets Platelets in the blood kiloplatelets/mL [25.01, ..., 850.00]
Serum creatinine Level of creatinine in the blood mg/dL [0.50, ..., 9.40]
Serum sodium Level of sodium in the blood mEq/L [114, ..., 148]
Smoking If the patient smokes Boolean 0, 1
Time Follow-up period Days [4,...,285]
(target) death event If the patient died during the follow-up period Boolean 0, 1
mcg/L: micrograms per liter. mL: microliter. mEq/L: milliequivalents per litre
Chicco and Jurman BMC Medical Informatics and Decision Making (2020) 20:16 Page 4 of 16
Table 2 Statistical quantitative description of the category features
Full sample Dead patients Survived patients
Category feature # % # % # %
Anaemia (0: false) 170 56.86 50 52.08 120 59.11
Anaemia (1: true) 129 43.14 46 47.92 3 40.89
High blood pressure (0: false) 194 64.88 57 59.38 137 67.49
High blood pressure (1: true) 105 35.12 39 40.62 66 32.51
Diabetes (0: false) 174 58.19 56 58.33 118 58.13
Diabetes (1: true) 125 41.81 40 41.67 85 41.87
Sex (0: woman) 105 35.12 34 35.42 71 34.98
Sex (1: man) 194 64.88 62 64.58 132 65.02
Smoking (0: false) 203 67.89 66 68.75 137 67.49
Smoking (1: true) 96 32.11 30 31.25 66 32.51
#: number of patients. %: percentage of patients. Full sample: 299 individuals. Dead patients: 96 individuals. Survived patients: 203 individuals.
end of the follow-up period, that was 130 days on aver-
age [52]. The original dataset article [52] unfortunately
does not indicate if any patient had primary kidney dis-
ease, and provides no additional information about what
type of follow-up was carried out. Regarding the dataset
imbalance, the survived patients (death event = 0) are
203, while the dead patients (death event = 1) are 96. In
statistical terms, there are 32.11% positives and 67.89%
negatives.
As done by the original data curators [52], we repre-
sented this dataset as a table having 299 rows (patients)
and 13 columns (features). For clarification purposes, we
slightly changed the names of some features of the origi-
nal dataset (Additional file 1). We report the quantitative
characteristics of the dataset in Table 2 and Table 3. Addi-
tional information about this dataset can be found in the
original dataset curators publication [52, 66].
Methods
In this section, we first list the machine learning methods
we used for the binary classification of the survival (“Sur-
vival prediction classifiers” section), and the biostatistics
and machine learning methods we employed for the fea-
ture ranking (“Feature ranking” section), discarding each
patient’s follow-up time. We then describe the logistic
regression algorithm we employed to predict survival
and to perform the feature ranking as a function of the
follow-up time (“Stratified logistic regression” section).
We implemented all the methods with the open source
R programming language, and made it publically freely
available online (Data and software availability).
Survival prediction classifiers
This part of our analysis focuses on the binary prediction
of the survival of the patients in the follow-up period.
To predict patients survival, we employed ten dif-
ferent methods from different machine learning areas.
The classifiers include one linear statistical method (Lin-
ear Regression [71]), three tree-based methods (Random
Forests [72], One Rule [73], Decision Tree [74]), one
Artificial Neural Network (perceptron [75]), two Support
Vector Machines (linear, and with Gaussian radial ker-
nel [76]), one instance-based learning model (k-Nearest
Neighbors [77]), one probabilistic classifier (Naïve Bayes
Table 3 Statistical quantitative description of the numeric features
Full sample Dead patients Survived patients
Numeric feature Median Mean σ Median Mean σ Median Mean σ
Age 60.00 60.83 11.89 65.00 65.22 13.21 60.00 58.76 10.64
Creatinine phosphokinase 250.00 581.80 970.29 259.00 670.20 1316.58 245.00 540.10 753.80
Ejection fraction 38.00 38.08 11.83 30.00 33.47 12.53 38.00 40.27 10.86
Platelets 262.00 263.36 97.80 258.50 256.38 98.53 263.00 266.66 97.53
Serum creatinine 1.10 1.39 1.03 1.30 1.84 1.47 1.00 1.19 0.65
Serum sodium 137.00 136.60 4.41 135.50 135.40 5.00 137.00 137.20 3.98
Time 115.00 130.30 77.61 44.50 70.89 62.38 172.00 158.30 67.74
Full sample: 299 individuals. Dead patients: 96 individuals. Survived patients: 203 individuals. σ : standard deviation
Chicco and Jurman BMC Medical Informatics and Decision Making (2020) 20:16 Page 5 of 16
[78]), and an ensemble boosting method (Gradient Boost-
ing [79]).
We measured the prediction results through common
confusion matrix rates such as Matthews correlation
coefficient (MCC) [80], receiver operating characteristic
(ROC) area under the curve, and precision-recall (PR)
area under the curve (Additional file 1) [81]. The MCC
takes into account the dataset imbalance and generates a
high score only if the predictor performed well both on
the majority of negative data instances and on the major-
ity of positive data instances [82–84]. Therefore, we give
more importance to the MCC than to the other confusion
matrix metrics, and rank the results based on the MCC.
Feature ranking
For the feature ranking, we employed a traditional univari-
ate biostatistics analysis followed by a machine learning
analysis; afterwards, we compared the results of the two
approaches.
Biostatistics. We used common univariate tests such
as Mann–Whitney U test [85], Pearson correlation coeffi-
cient [86], and chi square test [87] to compare the distri-
bution of each feature between the two groups (survived
individuals and dead patients), plus the Shapiro–Wilk test
[88] to check the distribution of each feature. Each test
has a different meaning but all of them produce a score
(a coefficient for the PCC, and a p-value for the other
tests) representing the likelihood of a feature to be asso-
ciated to the target. These scores can then be employed
to produce a ranking, that lists the features from the most
target-related to the least target-related.
The Mann–Whitney U test (or Wilcoxon rank–sum
test) [85], applied to each feature in relation to the death
event target, detects whether we can reject the null
hypothesis that the distribution of the each feature for the
groups of samples defined by death event are the same. A
low p-value of this test (close to 0) means that the ana-
lyzed feature strongly relates to death event, while a high
p-value (close to 1) means the opposite. The Pearson cor-
relation coefficient (or Pearson product-moment correla-
tion coefficient, PCC) [86] indicates the linear correlation
between elements of two lists, showing the same elements
on different positions. The absolute value of PCC gener-
ates a high value (close to 1) if the elements of the two
lists have linear correlation, and a low value (close to 0)
otherwise.
The chi square test (or χ2 test) [87] between two fea-
tures checks how likely an observed distribution is due to
chance [89]. A low p-value (close to 0) means that the two
features have a strong relation; a high p-value (close to 1)
means, instead, that the null hypothesis of independence
cannot be discarded.
Similar to what Miguel and colleagues did on a breast
cancer dataset [90], we decided also to take advantage of
the Shapiro–Wilk test [88] to assess if each feature was
extracted from a normal distribution.
Machine learning. Regarding machine learning feature
ranking, we focused only on Random Forests [72, 91],
because as it turned out to be the top performing clas-
sifier on the complete dataset (“Feature ranking results”
section). Random Forests [72] provides two feature rank-
ing techniques: mean accuracy reduction and Gini impu-
rity reduction [92]. During training, Random Forests gen-
erates several random Decision Trees that it applies to
data subsets, containing a subsets both of data instances
and of features. In the end, Random Forests checks all the
binary outcomes of these decisions trees and chooses its
final outcome through a majority vote. The feature rank-
ing based upon the mean accuracy decreases counts how
much the prediction accuracy decreases, when a partic-
ular feature is removed. The method then compares this
accuracy with the accuracy obtained by using all the fea-
tures, and considers this difference as the importance of
that specific feature: the larger the accuracy drop, the
more important the feature. The other feature ranking
method works similarly, but is based upon the Gini impu-
rity decrease [91]: the more the Gini impurity drops, the
more important the feature.
Aggregate feature rankings and prediction on the top
features
Starting from the whole dataset D we generated a col-
lection D = {{Dtri , Dtsi
}}N
i=1 of N Monte Carlo stratified
training/test partitions D = Dtri ∪ Dtsi with ratio 70%/30%.
For each execution, we randomly selected 70% of
patients for the training set, and used the remaining 30%
for the test set. To make our predictions more realistic,
we avoided using the same balance ratio of the whole
complete dataset (32.11% positives and 67.89% negatives).
This way, we had different balance ratios for each of the
100 executions with, on average, 32.06% positives and
66.94% negatives on average in the training sets, and with,
on average, 32.22% positives and 67.78% negatives on
average in the test sets.
On the N training portions Dtr1 , . . . , D
tr
N we applied
seven different feature ranking methods, namely RRe-
liefF [93–95], Max-Min Parents and Children [96–98],
Random Forest [72], One Rule [73], Recursive Partition-
ing and Regression Trees [99], Support Vector Machines
with linear kernel [100] and eXtreme Gradient Boosting
[79, 101, 102], using the feature death event as the tar-
get and obtaining 7N ranked lists of the 11 features.
Agglomerating all the 7N features into the single Borda list
[103, 104] we obtained the global list (Fig. 2 for N =
100), together with the Borda count score of each fea-
ture, corresponding to the average position across all 7N
lists, and thus the lower the score, the more important the
feature.
Chicco and Jurman BMC Medical Informatics and Decision Making (2020) 20:16 Page 6 of 16
We then used only the top–two features, namely serum
creatinine and ejection fraction to build on each sub-
set Dtri three classifiers, namely Random Forests (RF),
Support Vector Machine with Gaussian Kernel (GSVM)
and eXtreme Gradient Boosting (XGB). Finally, we then
applied the trained models to the corresponding test por-
tions Dtsi with the aforementioned top–2 features and
averaged the obtained performances modelwise on the N
test set instances.
For the feature ranking and the classification made
on the top two features, we employed different sets
of the machine learning methods than the ones we
used for the survival prediction on the complete dataset
(“Survival prediction classifiers” section): RReliefF, Max-
Min Parents and Children, Random Forests, One Rule,
Recursive Partitioning and Regression Trees Support Vec-
tor Machines with linear kernel, and eXtreme Gradient
Boosting, for the feature ranking, and Random Forests,
Gradient Boosting, and SVM with radial kernel. We
decided to use three different sets of methods because we
aimed to demonstrate the generalisability of our approach,
by showing that our computational solution is not only
valid with few machine learning classifiers, but rather
works for several groups of methods.
Regarding the final prediction using only the top two
selected features, we chose Random Forests because
it resulted in being the top performing classifier on
the complete feature dataset (“Survival machine learn-
ing prediction on all clinical features” section) and it
is universally considered an efficient method for fea-
ture ranking [92]. We then chose Gradient Boosting
and Support Vector Machine with radial Gaussian kernel
because both these methods have shown efficient per-
formances in feature ranking with medical informatics
data [105, 106].
Stratified logistic regression
In the just-described first analysis, we wanted to predict
the survival of patients and to detect the clinical feature
importance in the follow-up time, without considering its
different extent for each patient. In the second analysis,
we decided to include the follow-up time, to see if the
survival prediction results or the feature ranking results
would change. To analyze this aspect, we mapped the orig-
inal dataset time feature (containing the days of follow-up)
into a month variable, where month 0 means that fewer
than 30 days have gone by, month 1 means between 30
and 60 days, month 2 means between 60 and 90 days, and
so on.
We then applied a stratified logistic regression [107]
to the complete dataset, including all the original clini-
cal features and the derived follow-up month feature. We
measured the prediction with the aforementioned confu-
sion matrix metrics (MCC, F1 score, etc.), and the feature
ranking importance as the logistic regression model coef-
ficient for each variable.
Results
In this section, we first describe the results we obtained
for the survival prediction on the complete dataset
(“Survival machine learning prediction on all clinical fea-
tures” section), the results obtained for the feature rank-
ing (“Feature ranking results” section), and the results
on the survival prediction when using only the top two
most important features of the dataset (“Survival machine
learning prediction on serum creatinine and ejection frac-
tion alone” section and “Serum creatinine and ejection
fraction linear separability” section), all independently
from the follow-up time. We then report and discuss
the results achieved by including the follow-up time of
each patient in the survival prediction and feature rank-
ing (“Survival prediction and feature ranking including the
follow-up period” section).
Survival machine learning prediction on all clinical features
We employed several methods to predict the survival
of the patients. We applied each method 100 times and
reported the mean result score (Table 4).
For methods that needed hyper-parameter optimization
(neural network, Support Vector Machine, and k-Nearest
Neighbors), we split the dataset into 60% (179 randomly
selected patients) for the training set, 20% (60 randomly
selected patients) for the validation set, and 20% (the
remaining 60 patients) for the test set. To choose the top
hyper-parameters, we used a grid search and selected the
models that generated the highest Matthews correlation
coefficient [83].
For the other methods …
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Note: The requirements outlined below correspond to the grading criteria in the scoring guide. At a minimum
Chen
Read Connecting Communities and Complexity: A Case Study in Creating the Conditions for Transformational Change
Read Reflections on Cultural Humility
Read A Basic Guide to ABCD Community Organizing
Use the bolded black section and sub-section titles below to organize your paper. For each section
Losinski forwarded the article on a priority basis to Mary Scott
Losinksi wanted details on use of the ED at CGH. He asked the administrative resident