Dimensionality Reduction in Supervised Models-based for Heart
Failure Prediction
Anna Karen Garate Escamilla
, Amir Hajjam El Hassani
and Emmanuel Andres
Nanomedicine Lab, Univ. Bourgogne Franche-Comte, UTBM, F-90010 Belfort, France
Service de Médecine Interne, Diabète et Maladies métaboliques de la Clinique Médicale B, CHRU de Strasbourg,
Strasbourg, France
Centre de Recherche Pédagogique en Sciences de la Santé, Faculté de Médecine de Strasbourg, Université de Strasbourg
(UdS), Strasbourg, France
Keywords: Machine Learning, Heart Failure, Apache Spark, Feature Selection, PCA.
Abstract: Cardiovascular diseases are the leading cause of death worldwide. Therefore, the use of computer science,
especially machine learning, arrives as a solution to assist the practitioners. The literature presents different
machine learning models that provide recommendations and alerts in case of anomalies, such as the case of
heart failure. This work used dimensionality reduction techniques to improve the prediction of whether a
patient has heart failure through the validation of classifiers. The information used for the analysis was
extracted from the UCI Machine Learning Repository with data sets containing 13 features and a binary
categorical feature. Of the 13 features, top six features were ranked by Chi-square feature selector and then
a PCA analysis was performed. The selected features were applied to the seven classification models for
validation. The best performance was presented by the ChiSqSelector and PCA models.
The WHO (World Health Organization, 2018) lists
cardiovascular diseases as the leading cause of death
worldwide with 17.7 million people dying every
year. Heart diseases are affected by alcohol
consumption, tobacco use, lack of exercise, an
unhealthy diet and are present in people with high
blood pressure, high blood glucose, overweight and
obesity. A well-known cardiovascular disease is
heart failure. Heart failure (HF) is a chronic
condition present when the heart cannot pump
enough blood to meet the necessity of the body. The
American Heart Association lists the symptoms of
HF such as shortness of breath, weight gain (1 or 2
kg. per day), fatigue, trouble sleeping, swelling in
the legs, chronic cough and high heart rate (Heart,
2018). The diagnosis of heart failure can be a
problem for the practitioners given its nature of
being common or confused with the signs of aging.
The growth in the collection of medical data
presents a new opportunity for doctors to improve
the diagnosis of patients. In recent years, machine
learning has become an important solution in the
healthcare industry. Machine learning is an
analytical tool that works to help users identify
patterns and relationships by learning from
experience. It is used when the task is very large and
complex to program, such as the transformation of
medical files into knowledge, pandemic predictions
and genomic data analysis (Shalev-Shwartz et al.,
In the past, different studies have been done
using machine learning techniques to diagnose
different cardiac issues and predict the outcome. The
study of Rahhal et al. (2016) proposed a
classification of electrocardiogram (ECG) signals
through a deep neural network (DNN). Khalaf et al.
(2015) classified cardiac arrhythmias using
computer-aided diagnostic (CAD) systems to
categorize five types of beats. The prediction was
with support vector machine (SVM), obtaining an
accuracy of 98.60% with raw data, 96.30% with
PCA and 97.60% with Fisher Score (FS). Guidi et
al. (2014) proposed a clinical decision support
system (CDSS) for the analysis of HF. The best
accuracy was 87.6% using the CART model.
Parthiban and Srivatsa (2012) used a SVM technique
to diagnose heart disease in patients with diabetes,
obtaining an accuracy of 94.60%.
Escamilla, A., El Hassani, A. and Andres, E.
Dimensionality Reduction in Supervised Models-based for Heart Failure Prediction.
DOI: 10.5220/0007313703880395
In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2019), pages 388-395
ISBN: 978-989-758-351-3
2019 by SCITEPRESS Science and Technology Publications, Lda. All r ights reserved
The main problem of machine learning is the
high dimensionality (Domingos, 2012). The key to
the success of machine learning models is to select
the best features. It can be observed in the literature
that the use of feature selection techniques helped
the performance of a classification algorithm in the
prediction of HF. Dun et al. (2016) used deep
learning, random forest, logistic regression, SVM
and neural network with hyperparameters and
feature selection to predict the presence of heart
disease, obtaining an accuracy of 78%. Yaghouby et
al. (2009) classified arrhythmias using the
generalized discriminant analysis (GDA) and the
multilayer perception (MLP) neural network with an
accuracy of 100%. Rajagopal et al. (2017) presented
a classification of cardiac arrhythmia using five
different linear and non-linear unsupervised
dimensionality reduction techniques combined with
a probabilistic neural network (PNN) classifier. The
PNN classifier and the fast independent component
analysis (fastICA) obtained the best result with
99.83%. Singh et al. (2018) computed better results
in the detection of coronary heart disease using
reduction functions with 100% accuracy. Asl et al.
(2008) presented a classification that used 15
features extracted from heart rate variability (HRV)
signal. The authors reduced the features to five using
a GDA technique and increased the accuracy to
100% when combined with the SVM classifier.
This paper proposes the combination of
classification models with dimensionality reduction
techniques to achieve two main objectives: (1) to
learn the best feature representation of the data set
used; and (2) to use machine learning techniques as
a classifier to obtain the best possible prediction.
The data set used to achieve this purpose came from
the UCI Machine Learning Repository (UCI, 2018)
and is computed with seven classifiers: logistic
regression, decision tree, random forest, gradient-
boosted tree, multilayer perception, one-vs-rest and
Naïve Bayes. The feature selection technique of Chi-
square is implemented and used by PCA.
Remaining of this paper is organized as follows.
A short summary of the models used are explained
in Section 2. Detail descriptions of the methodology
are presented in Section 3. Experimental results are
reported in Section 4. Finally, Section 5 concludes
the work.
The classifiers models used in this paper are
presented in this section.
2.1 Logistic Regression
Logistic regression (Hastie et al., 2017) is a binary
classification response used to describe information
and explain the relationship between dependent and
independent variables. For a binary classification,
the model makes predictions by applying the logistic
2.2 Decision Tree
Decision tree is a classification and regression
method commonly used for machine learning
because its nature of being easy to interpret. The tree
predicts the label for each partition (leaf), and each
one is chosen by selecting the best split of the
different possible splits. Each tree node is chosen
from the set 
where 
is the
information obtained when a split s is applied to a
dataset D (Marsland et al., 2015).
2.3 Random Forest
Random forest (Breiman, 2001) is a collection of
decision trees predictors in which each tree depends
on the value of an independent random vector. The
training algorithm works in a parallel and random
mode, making each decision tree different and with a
reduction in variance.
2.4 Gradient-Boosted Tree
Gradient-Boosted Trees (GBTs) are ensembles of
decision trees which minimize a loss function
(Friedman, 1999). The mechanism used to reduce
the loss function in the training data is given by
where =number of instances,
=label of instance
=features of instance ,
=model’s predicted
label for instance .
2.5 Multilayer Percepton
Multilayer perceptron classifier (Hornik, 1991)
is based on the feedforward artificial neural
network and consists of multiple layers of nodes.
Each layer is connected to the next layer. The input
Dimensionality Reduction in Supervised Models-based for Heart Failure Prediction
data is represented by the nodes in the input layer.
The other nodes map the input to the output
by combining the weight w and the bias of the
node. This is written as a matrix with +1 layers
The nodes in intermediate layers use a sigmoid
function given by
The nodes in the output layer use softmax
function given by
where corresponds to the number of classes.
2.6 One-vs-Rest
One-vs-Rest is a classifier that creates a binary
classification problem for each class. One-vs-Rest
converts one class as positive and the rest of the
classes as negative. The classifier with the highest
value will be the output.
2.7 Naïve Bayes
Naïve Bayes is a classifier based on the theorem of
Bayes with strong independence assumptions
between the features. It works with the assumption
of using observation of the problem to make a
prediction (Marsland, 2018).
The data set used in the research is the “Heart
Disease Data set” from the UCI Machine Learning
Repository. The data set contains 76 features, but
most of the existing articles used only the subset of
14 features described in Table 1. The categorical
feature Num contains whether a patient has a
presence or absence of a heart disease. The
categorical features 1, 2, 3 and 4 of the original data
set were transformed in one that is the presence (1)
of heart disease.
The data sets used are from hospitals in
Cleveland, Hungarian, Switzerland and Long Beach
VA. This study adds one more data set: Cleveland-
Hungarian (a combination of Cleveland and
Hungarian data sets with 597 patients). The most
common data set in other studies is Cleveland,
which has great data quality. On the contrary,
Hungarian, Switzerland and Long Beach VA have
Table 1: Features of Heart Disease Data set.
Number Code Feature Description
1 Age Age Age in years
2 Sex Sex 1=male; 0=female
3 Cp Chest pain type
1= typical angina; 2=atypical angina;
3=non-angina pain; 4= asymptomatic
4 Trestbps Resting blood pressure (mg) At the time of admission in hospital
5 Chol Serum cholesterol (mg)
6 Fbs Fasting blood sugar>120 mg/dl 1=yes; 0=no
7 Restecg Resting electrocardiographic results
0=normal; 1= ST-T wave abnormal;
2=left ventricular hypertrophy
8 Thalach Maximum heart rate achieved
9 Exang Exercise induced angina 1=yes; 0=no
10 Oldpeak ST depression induced by exercise relative to rest
11 Slope The slope of the peak exercise ST segment 1=upsloping; 2=flat; 3=downsloping
12 Ca
Number of major vessels (0-3) colored by
13 Thal Exercise thallium scintigraphy
3=normal; 6=fixed defect;
7=reversible defect
14 Num The predicted attribute 0=no presence; 1=presence
ICPRAM 2019 - 8th International Conference on Pattern Recognition Applications and Methods
several missing values and are less used. The Table 2
contains the number of patients per data set and the quality
of their data. The considerations taken for data cleansing
were to assign a unique category for
the missing values
and create rules considering the coherence of the
data. An example of this is that a patient cannot have
a cholesterol equal to zero.
The models and algorithms used were computed
using the MLlib guide of Apache Spark 2.2.0 and
programmed in Java language (Spark, 2018). This
library provides the classification and dimensionality
reduction tools to obtain the performance.
Table 2: Heart Disease Data set.
Data Set Patients Quality of the data
Cleveland 303 Complete
Hungarian 294 Some feature are incomplete
(slope, ca and thal)
Switzerland 123 Some feature are incomplete
(thalach, chol, exang, slope,
ca and thal)
Long Beach
200 Some feature are incomplete
(fbs, ca and thal)
3.1 Dimensionality Reduction
Dimensionality reduction (Domingos, 2018) is the
process of reducing the number of variables under
consideration. It can be used to extract latent
features of raw data sets or compressing data while
maintaining the structure. This research proposed
two different dimensionality reduction methods, for
the feature selection, the Chi-square test of indepen-
dence was selected and for feature extraction, the
principal component analysis (PCA). After several
attempts, Chi-square test, with k=6, performs better
than other feature selection techniques in the
literature. The Chi-square test order features based
on the class and filters the top features of which the
class label depends on the most. ChiSqSelector
(ChiSq) of Apache Spark MLlib is used for feature
selection in model construction.
The list of the reduced set of features is shown in
Table 3. The order of the features in each row is
selected from most to least important. Further, the
six features were validated using seven classifiers
depicted in the next section. It can be observed that
chest pain (cp) is common in all data sets with the
exception of Switzerland. Similar to cp, cholesterol
(chol), maximum heart rate achieved (thalach), the
ST depression induced by exercise relative to rest
(oldpeak) are common. The values of exercise
induce angina (exang), exercise thallium
scintigraphy (thal) and the number of major vessels
colored by fluoroscopy (ca) were the results of the
non-invasive test to determine if the patient has heart
failure. Due to the poor quality of data, with the
exception of Cleveland, these features do not have
high rank.
PCA is a statistical procedure that converts the
correlated features into a new set of uncorrelated
features with the aim of losing the less amount of
information. The PCA class trains a model to project
vectors to a low-dimensional space. After several
test, the best result for PCA was given by the
features selected of ChiSq to create the components
instead of using the raw data.
Table 3: Features selected by ChiSq.
Data set List of features
Cleveland chol, thalach, oldpeak, thal, cp, ca
Hungarian chol, slope, exang, oldpeak, thalach,
Long Beach VA chol, thalach, age, trestbps, oldpeak,
Switzerland thalach, oldpeak, age, cp, trestbps,
chol, oldpeak, cp, exang, slope,
3.2 Classifiers Proposed
For this research, the libraries of ML Spark are used
to make the predictions. The classification models
were computed with default value of their
hyperparameters. The models are: (1) decision tree
(DT); (2) gradient-boosted tree (GBT); (3) logistic
regression (LOG); (4) multilayer percepton (MPC);
(5) Naive Bayes (NB); (6) one-vs-rest (OvR); and
(7) random forest (RF).
For experimentation, the data sets are divided in
70% for training, of which 80% is used for training
and 20% for validation, and 30% for testing. The
classifiers run 10 times and evaluate the accuracy,
precision, recall and F1 score of the categorical
feature, observing the percentage of the correct
classification. The confusion matrix reports the basic
terms used by these evaluations: (1) true positives
(TP) are cases in which the patients have heart
disease and are correctly predicted; (2) true
negatives (TN) are patients who do not have a heart
disease and are predicted as negative; (3) false
positives (FP) are patients predicted as positive, but
do not have heart disease; and (4) false negatives
(FN) are patients predicted as negative, but they
have a heart disease.
Dimensionality Reduction in Supervised Models-based for Heart Failure Prediction
Table 4: Cleveland predictions.
Features Performance DT GBT LOG MPC NB OvR RF
Accuracy (%) 84.3 83.5 88.7 84.5 72.7 89.7 90.4
Precision (%) 88.6 86.8 88.5 78.4 67.9 94.1 83.8
Recall (%) 77.5 78.6 85.2 85.1 86.4 84.2 88.6
F1 (%) 82.7 82.5 86.8 81.6 76.0 88.9 86.1
Accuracy (%) 84.3 83.8 89.3 87.5 78.0 90.9 87.4
Precision (%) 86.8 78.9 92.3 89.5 90.0 94.6 83.3
Recall (%) 78.6 85.7 85.7 82.9 64.3 88.1 89.7
F1 (%) 82.5 82.2 88.9 86.1 75.0 91.2 86.4
Accuracy (%) 85.1 83.9 88.6 90.3 75.3 88.4 87.8
Precision (%) 86.1 85.0 93.1 89.3 71.9 89.7 88.4
Recall (%) 77.5 79.1 81.8 86.2 83.7 83.3 79.2
F1 (%) 81.6 81.9 87.1 87.7 77.4 86.4 83.5
Table 5: Hungarian predictions.
Features Performance DT GBT LOG MPC NB OvR RF
Raw data
Accuracy (%) 83.5 86.0 92.0 87.8 85.9 89.7 90.5
Precision (%) 76.5 83.8 95.8 86.7 78.8 81.1 88.9
Recall (%) 81.3 79.5 76.7 72.2 81.3 90.9 80.0
F1 (%) 78.8 81.6 85.2 78.8 80.0 85.7 84.2
Accuracy (%) 85.9 86.7 91.0 86.3 83.8 89.0 86.6
Precision (%) 75.9 75.0 95.5 80.8 70.9 86.2 89.3
Recall (%) 88.0 87.5 75.0 77.8 84.6 80.6 71.4
F1 (%) 81.5 80.8 84.0 79.2 83.3 84.4 80.0
Accuracy (%) 86.0 84.8 92.2 89.2 84.9 89.8 89.8
Precision (%) 88.5 85.0 85.0 81.1 79.5 88.5 95.8
Recall (%) 71.9 85.0 85.0 81.1 86.1 79.3 71.9
F1 (%) 79.3 85.0 85.0 81.1 82.7 83.6 82.1
The accuracy rate is computed using the formula
given by
 =
 +
 + + +
The precision is the positive predicted value
defined by
 =
The recall is defined as the proportion of patients
with heart disease correctly identified given by
 =
F1 score is given by the precision in Eq. (7) and
recall in Eq. (8), considering an harmonic average
defined by
1 = 2
The data sets are computed using the supervised
machine learning algorithms. These 13 features are
reduced to six new features or components using: (1)
PCA algorithm with the features of ChiSq and (2)
the top six features of the ChiSq. Finally, the
classifiers validate the performance.
Table 4 contains the best performance of the
Cleveland data set. In most cases, the best accuracy
and F1 score improves when dimensionality
reduction techniques are applied, with the exception
of RF that computes the best accuracy using raw
data with 90.4% accuracy and 86.1% F1 score. The
distribution of information in Cleveland is uniform,
which leads to similar accuracy and F1score.
Overall, the best performance was using ChiSq-
PCA-OvR with an accuracy of 90.9%, a precision of
88.1%, a recall of 88.1% and a F1 score of 91.2%.
The most notable improvements presented by the
dimensionality reduction techniques, compared to
the raw data, were for PCA the computations of
ICPRAM 2019 - 8th International Conference on Pattern Recognition Applications and Methods
Table 6: Long Beach VA predictions.
Features Performance DT GBT LOG MPC NB OvR RF
Accuracy (%) 84.3 80.0 85.9 83.1 76.7 82.6 84.2
Precision (%) 88.9 80.0 80.0 66.7 43.8 57.1 50.0
Recall (%) 53.3 28.6 50.0 42.9 50.0 30.8 27.3
F1 (%) 66.7 42.1 61.5 52.2 46.7 40.0 35.3
Accuracy (%) 82.0 80.6 84.3 85.9 71.0 83.3 85.2
Precision (%) 57.9 66.7 87.5 63.6 43.7 75.0 72.7
Recall (%) 78.6 52.6 41.2 50.0 43.7 40.0 50.0
F1 (%) 66.7 58.8 56.0 56.0 43.7 52.2 59.3
Accuracy (%) 86.0 79.4 85.1 83.6 76.3 83.3 87.5
Precision (%) 77.8 50.0 80.0 75.0 52.9 58.3 85.7
Recall (%) 58.3 57.1 40.0 42.9 60.0 58.3 54.5
F1 (%) 66.7 53.3 53.3 54.5 56.3 58.3 66.7
Table 7: Switzerland predictions.
Features Performance DT GBT LOG MPC NB OvR RF
Raw data
Accuracy (%) 94.1 97.0 96.8 96.3 73.2 97.1 97.1
Precision (%) 50.0 100.0 100.0 100.0 26.7 100.0 100.0
Recall (%) 100.0 50.0 75.0 33.3 100.0 50.0 33.3
F1 (%) 66.7 66.7 85.7 50.0 42.1 66.7 50.0
Accuracy (%) 94.3 93.8 100.0 97.3 97.0 97.3 97.4
Precision (%) 66.7 50.0 100.0 100.0 100.0 100.0 100.0
Recall (%) 66.7 50.0 100.0 33.3 75.0 33.3 33.3
F1 (%) 66.7 50.0 100.0 50.0 85.7 50.0 50.0
Accuracy (%) 93.5 94.7 96.9 100.0 92.5 95.8 97.4
Precision (%) 100.0 50.0 100.0 100.0 100.0 100.0 100.0
Recall (%) 33.3 50.0 33.3 100.0 20.0 33.3 50.0
F1 (%) 50.0 50.0 50.0 100.0 33.3 50.0 66.7
LOG and OvR, with an increase of 0.6% and 1.2%
respectively. In the case of ChiSq were DT and
MPC, with an increase of 0.8% and 5.8%.
The best results of the Hungarian data set are
presented in Table 5. The best accuracy are
computed using dimensionality reduction techniques
in almost all the cases with the exception of RF and
NB, which presented the best accuracy with 90.5%
and 85.9% respectively. In general, the best
accuracy results are computed by ChiSq-LOG with
92.2% accuracy and 85.0% of precision, recall and
F1 score. The lack of uniform distribution shows
that ChiSq-LOG has a better performance compared
to raw data, which obtains a poor recall with 76.7%.
Even if the performance is similar between raw data
and dimensionality reduction techniques, ChiSq
present the most remarkable results.
Table 6 shows the best results from the Long
Beach VA data set. In general, dimensionality
reduction techniques present better results than raw
data, with the exception of LOG and NB that
compute the best accuracy with 85.9% and 76.7%
respectively. Overall, ChiSq-RF calculates the best
accuracy with 87.5% and a recall and F1 score with
one of the highest values with 85.7% and 66.7%
respectively. In Long Beach VA, the results of
precision, recall and F1 score were considerably
low, this was due to a small rate of true positives
and, therefore, this data set and models have a poor
performance. The most notable improvements are,
compared to the raw data, for PCA the computation
of MPC with an increase of 2.8%. ChiSq increases
1.7% with DT and 3.3% with RF.
The best performance of the Switzerland data set
is shown in Table 7. The Switzerland data set has
better accuracy than the other data set presented, this
can be explained by the presence of heart disease in
113 of the 123 patients, which means that the
database is unbalanced. Due to the lack of
uniformity, the tests obtain the greatest gap between
accuracy and F1 score with a difference of around
40%. PCA compute the best accuracy in most of the
cases, except for GBT with 97% using raw data. The
best results are presented by ChiSq-PCA-LOG and
ChiSq-MPC without errors, obtaining an accuracy,
precision, recall and F1 score of 100%.
Table 8 presents the best results for the
Cleveland-Hungarian data set. In general, ChiSq
Dimensionality Reduction in Supervised Models-based for Heart Failure Prediction
Table 8: Cleveland-Hungarian predictions.
Features Performance DT GBT LOG MPC NB OvR RF
Raw data
Accuracy (%) 82.0 83.7 83.9 83.7 68.6 86.0 87.6
Precision (%) 78.0 76.5 83.6 79.0 58.5 77.0 87.2
Recall (%) 76.7 81.3 76.7 85.3 92.0 87.7 85.0
F1 (%) 77.3 78.8 80.0 82.1 71.5 82.0 86.1
Accuracy (%) 82.1 81.4 87.0 84.4 66.9 85.7 79.6
Precision (%) 77.5 81.2 86.7 77.8 58.9 82.1 82.7
Recall (%) 77.5 70.9 79.3 86.3 91.2 82.1 69.7
F1 (%) 77.5 75.7 82.8 81.8 71.6 82.1 75.6
Accuracy (%) 84.1 82.5 86.5 85.6 80.3 86.7 84.6
Precision (%) 73.6 83.2 88.9 79.1 75.4 81.4 89.1
Recall (%) 85.5 77.0 77.8 84.1 73.1 83.8 74.0
F1 (%) 79.1 80.0 83.0 81.5 74.2 82.6 80.9
compute better results over raw data and PCA,
except for GBT with 83.7% accuracy and RF with
87.6% accuracy. Overall, the best performance is RF
using raw data with an accuracy of 87.6%,
presenting remarkable values in precision, recall and
F1 with 85%, 87.2% and 86.1% respectively.
The most outstanding comparison with all the
features is the increase of PCA by 3.1% with LOG,
ChiSq by 2.1% with DT, 1.9% with MPC, 11.7%
with NB and 0.7 with OvR.
From the above results, almost all the classifiers
work better using dimensionality reduction
techniques. Only in one of the data sets, the best
result is obtained using raw data with the RF
compiler. In general, the dimensionality reduction
techniques computed the best increment in accuracy
using DT and LOG.
The results of the machine learning models
combined with the dimensionality reduction
techniques were: (1) the results of GBT and NB did
not improve in most cases to generalize that the use
of dimensionality reduction is better; (2) DT, LOG,
MPC and OvR improved when it was used with
PCA and ChiSq, LOG obtained better results with
PCA and MPC with ChiSq; and (3) in most of the
cases RF had the best results using all the features
than a dimensionality reduction technique. In terms
of quality of information, when the data is more
complete, as in the case of Cleveland, the results of
PCA are better than incomplete data sets.
The performance comparison of the Cleveland
data set is given in Table 9. Based on this
comparison, ChiSq-PCA-OvR approach had better
accuracy than the literature methods. Comparing the
same methods, the best accuracy in this research is
given by decision tree with 85.1% using ChiSq,
logistic regression with 89.3% using ChiSq-PCA,
Naïve Bayes with 78.0% using ChiSq-PCA, random
Table 9: Performance comparison of Cleveland.
Author Method Accuracy
Mutyala, et al.
Decision Tree
Khanna, et al. (2015) Logistic
Kodati, et al. (2018) Naive Bayes 83.70%
Khan, et al. (2016) Random Forest 89.25%
Ziasabounchi, et al.
ANFIS- Neuronal
Network + Fuzzy
rules in 5 layers
forest with 90.4% using raw data and multilayer
percepton with 90.3% using ChiSq. With the
exception of Naive Bayes, the results obtained show
improvement compared to the proposed by the
In this paper, we proposed the use of dimensionality
reduction techniques with machine learning
classifiers to predict whether a patient has HF or not.
The results presented by the ChiSq selector of
Apache Spark were marvelous. The features that
were persistent in all the data sets were chest pain
(cp), cholesterol (chol), maximum heart rate
achieved (thalach) and the ST depression induced by
exercise relative to rest (oldpeak). These features
most be considered important in the detection and
analysis of HF. A disadvantage in the feature
selection is the lack of data quality, especially in
Switzerland and Long Beach VA.
PCA obtained better results with the features of
ChiSq and when the data set does not have many
null values. The experimental results obtained with
ICPRAM 2019 - 8th International Conference on Pattern Recognition Applications and Methods
the classifiers improve, except with random forest
that showed a better accuracy and F1 score when it
was used with all the features. Overall, ChiSq and
PCA obtained the highest accuracy, precision, recall
and F1 score. LOG and RF were the classifiers that
computed the best performance.
In general, the greatest problem with the models
was the false negatives, this is important to consider,
it is better to have a good classification of the false
negatives than the false positives. For future
development, some experimental work will attempt
to model the physiological HF problem, which is
difficult to do with few features. In addition, these
models will be replicated in a big data health
environment and test its functioning with massive
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