Predicting 30-day Readmission in Heart Failure using Machine

Learning Techniques

Jon Kerexeta

1,2

, Arkaitz Artetxe

1,2

, Vanessa Escolar

, Ainara Lozano

and Nekane Larburu

1,2

Vicomtech-IK4 Research Centre, Mikeletegi Pasalekua 57, 20009, San Sebastian, Spain

Biodonostia Health Research Institute, P. Doctor Begiristain s/n, 20014 San Sebastian, Spain

Hospital Universitario de Basurto (Osakidetza Health Care System), Avda Montevideo 18, 48013, Bilbao, Spain

Keywords: Heart Failure, Machine Learning, Hospital Readmission, Risk Prediction, Classification.

Abstract: Heart Failure (HF) is a syndrome that reduces patients’ quality of life, and has severe impacts on healthcare

systems worldwide, such as the high rate of readmissions. In order to reduce the readmissions and improve

patients’ quality of life, several studies are trying to assess the risk of a patient to be readmitted, so that taking

right actions clinicians can prevent patient deterioration and readmission. Predictive models have the ability

to identify patients at high risk. Henceforth, this paper studies predictive models to determine the risk of a HF

patient to be readmitted in the next 30 days after discharge. We present two different approaches. In the first

one, we combine unsupervised and supervised classification and achieved AUC score of 0.64. In the second

one, we combine decision tree and Naïve Bayes classifiers and achieved AUC score of 0.61. Additionally, we

discover that the results improve when training the predictive models with different readmission’s threshold

outcome, reaching the AUC score of 0.73 when applying the first approach.

1 INTRODUCTION

Heart Failure (HF) is a clinical syndrome caused by a

structural and/or functional cardiac abnormality. It

results in a reduced cardiac output (i.e. inability of the

heart to pump the blood in the required amounts to

satisfy the requirements of the metabolism), and/or

elevated intracardiac pressures at rest or during stress.

Moreover, HF is associated with a decreased quality

of life which reduces physical and mental activity

(Ponikowski, 2016).

The prevalence of HF depends on the definition

applied, but it is approximately 1–2% of the adults in

developed countries, rising to ≥10% among people

>70 years of age (Ponikowski, 2016). Hence, due to

the population’s aging, it is expected an increasing of

HF patient’s number in the future. Furthermore, HF

patients often readmit after the discharge, with 56.6%

of annual readmission (Maggioni, 2016), which result

on high expenses for healthcare systems. For

instance, Maggioni et al. (Maggioni, 2016) estimated

that €11,867 were spent annually per HF patient, in

Italy during 2008-2012.

Considering the expected increment of HF

patient’s number and the cost associated to each

patient, HF will potentially become a big issue in

coming decades unless some actions are taken. In this

context, there is a growing interest in reducing the

readmission rates.

In this paper we focus on the risk assessment of

HF patients’ readmission using machine learning

techniques, so that this information could help

clinicians on managing their patients best by giving a

closer follow-up to those patients with higher risk.

This way, readmission rate can be potentially

reduced, improving the quality of life of HF patients.

This paper is structured as follows: Section 2 –

State of the Art summarizes how the HF readmission

risk is assessed in the literature. Section 3 – Dataset

presents the dataset applied and Section 4 – Proposed

Methods, describes the classifiers proposed in this

study. Section 5 – Results, provides the results of each

proposed method. Finally, in Section 6 – Conclusion

and Future Work, we discuss the conclusions and

future work that will follow this study.

2 STATE OF THE ART

There are a plethora of studies on readmission risk

prediction modelling. This section presents a brief

308

Kerexeta, J., Artetxe, A., Escolar, V., Lozano, A. and Larburu, N.

Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques.

DOI: 10.5220/0006542103080315

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF, pages 308-315

ISBN: 978-989-758-281-3

summary of studies in the context of HF patient

readmission prediction (see Table 1).

Mortazavi et al. (Mortazavi, 2016) compare

prediction techniques in HF readmission: logistic

regression (LR), Poisson regression (PR), random

forest (RF) and, boosting and random forest

combined hierarchically with support vector

machines (SVM). Their dataset have 977 patients and

236 attributes. They achieve 0.543 AUC value using

LR and 0.615 using boosting. They improve the

results using a readmission threshold of 180 days:

0.669 AUC using RF and 0.678 using boosting.

Zolfaghar et al. (Zolfaghar, 2013) investigate the

readmission risk in HF, defining the outcome of 30-

days readmission. They focus in the fact that data is

not well balanced (i.e., many more no readmissions

than readmissions, with a proportion of 1:5.7). To

solve this problem, they make more than one

classifier in different layers, achieving sensitivity (Se)

of 0.31 and specificity (Sp) of 0.81.

Zheng et al. (Zheng, 2015) create several

classifiers to estimate if a patient with HF would

readmit within 30 days. They use neural networks

(NN), SVM with different kernels and RF. They have

a dataset of 1641 patients that had an admission

because of HF, and of those, 316 patients readmitted

within 30-days because HF. The best result that they

report were obtained with particle swarm

optimization-SVM, achieving a Se = 0.08, Sp = 0.97

and accuracy of 0.78.

Meadem et al. (Meadem, 2013) present a study of

readmission prediction within 30-days (all cause), in

patients with HF. They focus on the feature extraction

of the data: attribute selection (with chi-square and

stepwise), missing value imputation (with clustering)

and data balancing (with over-sampling and under-

sampling). They compare the performance of three

different classifiers LR, SVM and NB. Their dataset

is composed of 8,600 patients and 49 attributes, after

an attribute reduction/selection process. The best

result reported is AUC= 0.64, with stepwise attribute

selection, clustering missing value imputation, over-

sampling data balancing and SVM classifier.

Krumholz et al. (Krumholz, 2000) try to predict

whether after HF admission, patients are going to

readmit within 6 month. They use random survival

forest and Cox regression. They use a sample of 2,176

patients which had a HF admission (if more than one,

only the first readmission was considered). They

created different risk levels, obtaining the precision

value of 0.31 in the best case.

Table 1: Summary of related studies and methodologies.

Author

Data

size

Method

AUC

Mortazavi et al.

977

LR, PR and RF

0.687

Zolfaghar et al.

9,770

SVM & NB

0.31*

Zheng et al.

1,641

NN, RF,SVM

0.35*

Meadem et al.

8,600

SVM, NB & LF

0.64

Krumholz et al.

2,176

Cox regression

0.31*

Amarasingham

et al.

1,372

Multivariate

analysis

0.45*

Sudhakar et al.

1,046

RR score

0.61

Artetxe et al.

119

RF & SVM

0.65

*Precision or Sensitivity values

Amarasingham et al. (Amarasingham, 2010)

estimated the 30-days readmission because of HF and

mortality risk using multivariate analysis. They

applied a dataset comprised of 1372 HF patients, of

which 331 readmitted and 43 died within 30-days

after discharge. After the multivariate analysis of the

data, they got a precision of 0.456 in the best quintile

that they created.

Sudhakar et al. (Sudhakar, 2015) develop an all-

cause readmission risk score (RR score) in people

with HF. They employ a sample of 1,046 admissions

(from 712 patients) because of HF, and of those, 369

readmitted within 30-days (all cause). They do a

multivariate analysis to get the RR score and 0.61 is

the best AUC value that they achieve.

Artetxe et al. (Artetxe, 2017) built classifiers to

identify HF patients that have high risk of

readmission caused by HF. They focus on the feature

extraction. They use filter, wrapping and embedding

methods to extract the features, and then, RF and

SVM classifiers. The best performance they achieve,

using a dataset of 119 cases, is AUC=0.647 with

Wrapping extraction method and linear SVM

classifier.

3 DATASET

The public hospital OSI Bilbao-Basurto (Osakidetza),

located in Basque Country (Spain), has been

gathering HF patients’ information from 2014 untill

2017. For the present study, the dataset contained a

cohort of 231 HF patients. Clinicians have collected

baseline data (information collected by a clinician

when the patient was diagnosed with HF, Table 2),

ambulatory patient monitored data (i.e. information

that patients at home collect from three to seven times

per week, e.g. heart rate) and patients’ admissions

information (i.e. information related to admission,

e.g. length of stay).

Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques

309

Table 2: Summary of the dataset.

Attribute

Description

Mean±SD

Age

The age of the patient

(years)

77.7±11

Sex

The sex of the patient

(men/women)

57% men*

Smoker

If the patient smoke,

did smoke and now do

not, or never has

smoked

18% Yes*

59% No*

23% Ex*

LVEF

Left Ventricular

Ejection Fraction (%)

42.2±15.3

FirstDiag

Years since first

diagnosis

6.15±7.23

Implanted

device

If the patient has

implanted a device

(yes/no)

23% Yes*

Need oxygen

If the patient needs

oxygen (yes/no)

6% Yes*

Laboratory

Urea

Urea (mg/dl)

73.7±37.7

Creatinine

Creatinine (mg/dl)

1.3±0.5

Sodium

Sodium (mEq/L)

140±4.2

Potassium

Potassium (g/dl)

4.27±0.76

Haemoglobin

Haemoglobin (g/dl)

13±10.25

Comorbidities

Sinus rhythm

If the patient has sinus

rhythm (yes/no)

37% Yes*

Comorbidity

A.F.

If the patient has atrial

fibrillation (yes/no)

57% Yes*

Pacemaker

If the patient has a

pacemaker (yes/no)

14% Yes*

*Proportions of the labels

In this study we aim to compare our results with

the state of the art by using the baseline information

(Table 2) and patients’ admissions. The dataset

contains 162 admissions caused by HF

decompensations, and of those admissions, 36 were

readmissions within 30-days caused by HF.

In addition to the attributes of Table 2, we added

the attributes previous admission and seasons to

determine whether these also have an impact on the

risk assessment.

The attribute previous admission consists in how

many admissions the patient had in the previous six

months. If the patient did not have any readmission in

the previous six months, would be the factor “none”.

If the patient had 1 to 2 admissions, would be the

factor “some readmissions”. Otherwise, if the patient

had more than 2 admissions in the previous six

months, would be the factor “many readmissions”.

The attribute seasons consists in the period at which

the patient was discharged:

▪ winter: discharge between 12/01 and 03/01

▪ spring: discharge between 03/01 and 06/01

▪ summer: discharge between 06/01 and 09/03

▪ autumn: discharge between 09/03 and 12/01

This attribute was included since clinicians have

noticed by experience that there are more

admissions/readmissions in autumn-winter than in

summer due to the incidence of respiratory infections,

which worsen heart failure. In summer, however,

hypotension is more frequent (when extreme

temperatures happen).

4 PROPOSED METHODS

In this section we present the two methods that we

propose to identify patients at high risk of being

readmitted. In Classifier with Clusters we combine

unsupervised and supervised classifiers. In Hybrid

Tree, we build a hybrid classifier using decision tree

and NB classifiers.

4.1 Classifier with Clusters

This approach combines different machine learning

methods to build a classifier. The scheme that it

follows is shown in Figure 1, and it is explained in the

next sections.

Figure 1: Classifier with Clusters’ scheme.

4.1.1 Clusters

Firstly, we apply Ward’s agglomerative hierarchical

clustering method (Ward, 1963) with Manhattan

distance to the dataset, from which we could

distinguish two significant clusters (Figure 2). Hence,

we work with each cluster separately, building a

“specialized” classifier for each one. This way when

a new patient risk will be assessed the applied

classifier will be the one that works best with similar

patients.

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Figure 2: Dendrogram of the data, using Ward’s method

and Manhattan distance.

In each cluster we evaluate three classification

algorithms, namely SVM (Vapnik, 2013), NB

(Murphy, 2006) and weighted Naïve Bayes (WNB)

(Zhang, 2004). In order to overcome the class

imbalance problem, we do random oversampling, and

since NB and WNB need discrete attributes, we apply

SVM-based discretization (Section 4.1.2).

4.1.2 Discretization

In order to apply NB and WNB classifiers, the

numeric attributes have to be discretized. For that, the

optimal cut-point is determined depending on the

outcome. To do this, we use a technique based on

SVM, similar to one used in Park & Lee (Park, 2009)

study, so that results in new binary attributes.

Moreover, we have to find a balance in SVM

(“new attributes”) number. With more “new factor

attributes” it is possible to have more information, but

if the attributes are combined to make SVM

classifiers, it is possible to obtain more accurate “new

factor attributes”. Hence, we have to decide how to

group the attributes when applying SVM classifiers.

In this study, Ward’s agglomerative hierarchical

method has been applied to group the attributes.

When two attributes are correlated, if they are

grouped the results may be better (separated could be

redundant). To determine their relation, we define the

distance between the attributes based on the

dependence between them:

𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑠) = 1 − 𝑎𝑏𝑠(𝑐𝑜𝑟(𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑠))

As a result, the dendrogram shown in Figure 3 is

obtained for Cluster 1 using this distance and Ward’s

method. The resulting groups are the following: (1)

FirstDiag, and sodium, (2) LVEF and potassium (3)

urea, creatinine, haemoglobin and age (Figure 3).

Same procedure is followed in Cluster 2 to group the

attributes.

Figure 3: Dendrogram of the attributes in Cluster 1.

Hereafter, SVM based discretization (Park, 2009) is

applied in each group.

4.1.3 Weights

Once all the attributes are discretized, it is possible to

apply NB classifier to the clusters. But in order to

study the impact of WNB, the weights of the

attributes have to be determined. For that, in this

study the weights are defined as the dependence

between attributes and the outcome.

To determine the level of the dependency between

attributes and the outcome, Cramer's V method

(Cramér, 2016) has been applied. This method does

not give only information on whether there is a

dependence, but also gives the degree of the

dependency, which is used as weights for the WNB.

4.2 Hybrid Tree

The clinicians involved in the project consider that

classifiers as NB or decision tree are easier to

interpret for them than other classifiers since they can

be interpreted as rules. Therefore, we develop a

classifier that combines both, called Hybrid Tree.

This classifier is a hybrid one. Firstly, we use a

tree classifier to reduce the data to similar elements.

Then, instead of giving the most frequent label, we

use a NB classifier to estimate the correct label. This

way we take advantage of all the attributes, instead of

Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques

311

Figure 4: The procedure scheme of the Hybrid Tree.

using only those that the tree requires, and we get

more accurate results.

Among all the studies that use similar hybrid tree

classifiers, the study of Kohavi et al. (Kohavi, 1996)

drew our attention. They compare the performance of

hybrid classifier with decision tree and NB classifiers

in 29 datasets with different number of elements

(100-8,000) and they find that the hybrid one

outperforms both (Tree classifier and NB).

4.2.1 Method

We use a recursive method, by which we perform the

following steps until a previously defined stopping

criterion is met:

We start with the data and the element to classify,

and we follow the procedure shown in Figure 4.

▪ Stop criterion: If the data has only one type

of label (1) or if the number of cases is too

small for further splits (2), we would stop,

and (1) return the label or (2) use NB.

▪ Discretize: We discretize all the numeric

attributes of the data in each step. We apply

SVM to each continuous attribute. Hence,

we convert into binary the continuous

attributes.

▪ The attribute: We have to choose with

which attribute we want to make the split.

For that, we calculate the dependency

between the discrete attributes and the

outcome, and we take the one with the

maximum correlation using Cramer’s V

method.

▪ Split the data: We take the cases from the

data that have the same selected attribute of

the element to classify, and go to stop

criterion.

▪ Naïve Bayes: If the stop criterion is met

because of the length of the data, we apply

NB classifier.

4.3 Evaluation Methodology

Due to the limitation on the number of cases from the

applied dataset, we use Leave One Out (Kearns,

1999) as model validation technique and sensitivity,

specificity, precision and area under ROC curve

(AUC) metrics (Zou, 2007) to evaluate the models.

Owing to the readmission problem, the evaluation

metrics are defined as follows:

▪ Sensitivity: among all the readmissions,

how many readmissions the classifiers has

labelled as readmission

▪ Specificity: among all the no readmissions,

how many the classifier has labelled as no

readmission

▪ Precision: among all labelled as

readmission, how many are correctly

identified as such.

5 RESULTS

In this section, we evaluate the proposed classifiers

described in Section 4, and additional experiments are

made varying the readmission threshold (THR).

5.1 Classifier with Clusters

5.1.1 Clustering

In this subsection, the differences between the

clusters built in Section 4.2.1 is studied. Firstly we

check whether there is any difference on the

attributes’ values from each of the clusters, and then

we see if the applied clustering method has an

influence on the outcome.

The biggest difference between clusters is

appreciated with urea attribute. In Cluster 2, all the

patients have the urea level higher than 80 (the mean

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is 113). In Cluster 1 only four patients have the Urea

level higher than 80 (the mean is 53). The urea level

determines renal function, which is closely related to

heart function.

In general, in cluster 2 we detect that most of the

attributes have “worse” values associated with worse

prognosis – most of them have higher value, except

LVEF, where low levels indicate a deterioration.

From this, we can suggest that a patient from cluster

2 has higher risk of readmission.

Besides, we have compared the proportions of the

discrete attributes of each cluster. We discover that,

for example, in Cluster 1 5.3% of the patients need

oxygen, while in Cluster 2 27.5% of the patients need

oxygen.

There are also significant differences in the

outcomes – readmission rate – which is a relevant fact

we consider. Figure 5 illustrates Kaplan-Meier curve

with the progression of the readmission in each

cluster. As presented, Cluster 2 has higher

readmission risk compared with those from Cluster 1

(Figure 5).

Figure 5: Kaplan-Meier of HF patient’s readmission rate for

the whole dataset, Cluster1 and Cluster 2.

5.1.2 Performance of the Classifier

Once the clustering is performed, we apply the

classifiers to the clusters and to all the dataset to check

whether clustering improves the results.

Table 3: Results we obtained with the classifiers.

Precision

AUC

With clusters

WNB

0.69

0.68

0.39

0.58

0.53

0.75

0.38

0.62

Comb

0.64

0.71

0.40

0.64

SVM

0.67

0.60

0.35

0.62

Without Clusters

WNB

0.38

0.79

0.34

0.62

0.33

0.88

0.44

0.61

SVM

0.5

0.73

0.35

0.60

As shown in Table 3, the best results are obtained

using the combined classifier, i.e. in one cluster WNB

and in the other NB. We also get suited results with

NB and SVM with clusters. Even if WNB's AUC is

lower than others, it has the highest sensitivity.

Therefore, we also consider it in our study.

If we compare the classifiers that use clusters with

those that do not use clusters, the results are very

similar in terms of AUC (Table 3). If we look at Se,

classifiers with clusters obtain higher scores, which is

very important because of the nature of the problem.

5.2 Hybrid Tree

In Section 4.2, we proposed the Hybrid Tree classifier

since clinicians involved consider it easier to interpret

the results if the cause is represented as a tree. The

results of the Hybrid Tree are Se=0.44, Sp=0.82,

precision=0.41 and AUC=0.61.

As presented in Section 5.2, similar AUC results

are obtained when employing Classifier with

Clusters. However, lower Se value is achieved with

Hybrid Tree. Therefore, it may be recommended the

usage of the first approach from the clinical point of

view.

5.3 Readmission-day Threshold

In order to visualize the impact of the readmission day

on the obtained results, we visually represent how the

implemented classifiers work, using as an example

the Kaplan-Meier curves obtained with the Hybrid

Tree (Figure 6).

Figure 6: Kaplan-Meier curve of the results of Hybrid Tree.

Predicting 30-day Readmission in Heart Failure using Machine Learning Techniques

313

Figure 6 represents the Kaplan-Meier curve of HF

patients dataset (purple) and the curves for those that

Hybrid Tree has classified as readmissions (in red),

and as no readmission (in green).

The no-readmission curve (in green) is expected

to be continuous at zero until the 30

day if the

prediction is 100% accurate, but even few of those

that are detected as no readmission readmit before 30

days. On the other hand, the readmission curve (in

red), should decrease all the way to 0 by the 30

day

for perfect prediction. But instead, there is no

readmission between approximately day 20-30, and

there is a big slope after day 30. Similar behaviour is

detected after the 30

THR when plotting Kaplan-

Meier curves for Classifiers with Clusters.

This way it is possible to notice from Figure 6 that

despite training the Hybrid Tree for 30-days

readmission THR, this THR may not be optimal.

Notice that this may also depend on the applied

dataset.

Furthermore, there is a precedent in the literature

(Mortazavi, 2016) that improves remarkably the

results training the classifier with 180-days of

readmission and testing with 30-days readmission.

Therefore, we decided to explore the presented

methods when the 30-day readmission THR is

modified (Section 5.4.1 and Section 5.4.2).

5.3.1 THR: Classifier with Clusters

Firstly, we check the Classifier with Clusters with

several THRs using the same THR for training and

testing each of them. The best results are obtained

with SVM using as THR 35-days (AUC = 0.788).

However, to evaluate the results with the outcome

of 30-days readmission (de facto standard) we train

the classifiers with different THRs and test how they

perform with 30-days readmission THR (Table 4).

Table 4: Classifiers with Clusters trained with different

readmission days THR and tested with 30-day readmission

THR.

Days

WNB

0.69

0.72

0.67

0.53

0.61

0.58

SVM

0.64

0.72

0.64

WNB

0.68

0.67

0.63

0.75

0.76

0.75

0.72

SVM

0.64

0.77

0.66

AUC

WNB

0.58

0.66

0.67

0.65

0.62

0.64

0.65

0.71

SVM

0.61

0.73

0.71

0.65

Table 4 presents the improvement of results,

where the best results are achieved using SVM

(outcome 35-days), with an AUC = 0.726, and Se =

0.72.

5.3.2 THR: Hybrid Tree

We also test different readmission THRs with Hybrid

Tree (Table 5). In this case also the results improve,

but the difference is not as high as with Classifier with

Clusters.

Table 5: Results of Hybrid Tree, training with different

readmission day THR.

Days

0.44

0.51

0.54

0.52

0.82

0.80

0.81

0.72

Precision

0.41

0.51

0.54

0.51

AUC

0.61

0.65

0.63

6 CONCLUSIONS AND FUTURE

WORK

In this study, we present two predictive models to

estimate the risk of 30-day readmission in Heart

Failure (HF) patients. The first approach combines

unsupervised and supervised classifiers (Classifiers

with Clusters), and the second one, combines decision

tree and Naïve Bayes (NB) classifiers (Hybrid Tree).

It has been discovered that training the predictive

models with different readmission day threshold

(higher than 30 days) the results may improve,

although it could be related to dataset limitations.

In this context, the best AUC score obtained in

this study has been 0.726 with Classifier with

Clusters (with Support Vector Machines in the

clusters), by training it with 35-days readmission

THR.

Furthermore, it is also observed that the results

substantially improve when the 30-day readmission

prediction THR is also extended. For example, when

training and testing with 35-days readmission, the

result is AUC = 0.788 applying SVM. As discussed,

this phenomenon could be due to the dataset size

limitations, but relevant to consider.

The AUC values for weighted Naïve Bayes

(WNB) and NB are similar. But with WNB the

sensitivity values are higher, and with NB, the

specificity values are higher. Hence, depending on the

problem’s nature, we could choose one of the

classifiers.

The Hybrid Tree classifier performs with an AUC

value of 0.65 if the training dataset is considered with

35 or 40 readmission’s days (with Se=0.51, Sp=0.80

and Se=0.54, Sp=0.81 respectively). However, the

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results of the Hybrid Tree may improve over time

when the size of the dataset increases (Kohavi, 1996).

Due to the nature of the problem the results do not

present very high predicting power. Nevertheless,

comparing this study with results from the state of the

art, the obtained results are satisfactory.

In the future, several actions are planned. First, the

presented classifiers will be trained with larger

amount of data as new patients are included into the

study. In parallel, the ambulatory patient monitored

data will be studied to determine whether the

presented predictive models could be improved.

Next, we aim to build an integrated telemonitoring

system that integrates these predictive models to

support both clinicians, to manage best the patients,

and patients, to empower them in their disease

management and prevent potential decompensations.

Finally, this system will be tested in a trial study to

determine its usability.

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