Prediction of Heart Disease Severity Using Hierarchically-Structured

Machine-Learning Models with Feature Space Reduction

Ayami Kiuchi

, Tomoya Fujita

and Hayato Yamana

Department of Computer Science and Engineering, Waseda University, Tokyo, Japan

Department of Computer Science and Communications Engineering, Waseda University, Tokyo, Japan

Faculty of Science and Engineering, Waseda University, Tokyo, Japan

Keywords: Heart Disease, Hierarchical Prediction, Feature Selection, Feature Space Reduction, mRMR, SMOTE.

Abstract: Heart disease is the primary cause of death worldwide according to the 2019 statistics published by the World

Health Organization (WHO), with roughly 8.9 million people dying annually. Predicting the likelihood and

severity of this disease leads to earlier detection and helps reduce the workload of medical professionals.

Previous studies have adopted a one-time classification that is insufficient to predict heart disease severity.

This study proposes a novel classification method to enhance the prediction accuracy of heart disease by

using: 1) a hierarchical binary-classification technique to classify the severity in order from the lowest level

and 2) a data-preprocessing technique to transform continuous values into binary values based on medical

knowledge and statistics information to decrease the feature space. An experimental evaluation of the heart-

disease dataset from the UC Irvine (UCI) machine-learning repository confirms that the proposed method

achieves the highest accuracy at 100% in predicting the presence of heart disease and at 93.13% in its severity

level. In addition, the proposed method achieved 96.67%, 91.25%, 90.59%, and 93.64% accuracy for severity

prediction in the Cleveland, Hungarian, Long-Beach-VA, and Switzerland datasets, respectively.

1 INTRODUCTION

Heart disease is the primary cause of death compared

with other illnesses according to a 2019 study by the

World Health Organization (WHO, 2019), with

approximately 8.9 million annual deaths. Compared

with two decades ago, heart disease-related deaths

have increased by two million, prompting the need for

immediate action. Early detection and treatment of

heart disease are essential to prevent death. However,

there is a significant disparity in the number of

physicians worldwide, with approximately 70 doctors

per 10,000 individuals in countries such as Sweden

and less than one doctor per 10,000 individuals in

African countries (WHO, 2019). This statistic

highlights a severe and ongoing shortage of medical

personnel. Consequently, efforts must be made to

address this shortage and reduce fatalities resulting

from heart disease by implementing diagnostic

prediction systems to assist medical professionals.

The UC Irvine (UCI) machine-learning repository

(UC Irvine Machine Learning Repository, 1988)

https://orcid.org/0000-0001-7542-4826

provides the most commonly used dataset for heart-

disease prediction, which targets the predictions of

presence and severity of heart disease.

The difficulty in predicting the presence and

severity of heart disease is that: 1) the amount of data

to be analyzed is small (920 in the UCI dataset) with

many features (76 in the UCI dataset) and 2) the

dataset is imbalanced in the severity of heart disease.

To address the prediction of heart disease,

previous studies engaged in dimensionality reduction

of features used for the machine-learning models

(Gupta et al., 2020; Balamurugan, Ratheesh, and

Venila, 2022). Wang et al. (Wang, Lauri, and Hassani,

2022) investigated five dimensionality reduction

methods for predicting the presence of heart disease

and concluded that minimum redundancy-maximum

relevance (mRMR) was the best.

In addition, previous studies have adopted the

synthetic minority oversampling technique (SMOTE)

to balance the positive and negative data for training

the model to increase prediction accuracy (Abdellatif

et al., 2022).

662

Kiuchi, A., Fujita, T. and Yamana, H.

Prediction of Heart Disease Severity Using Hierarchically-Structured Machine-Learning Models with Feature Space Reduction.

DOI: 10.5220/0012436700003657

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - Volume 2, pages 662-670

ISBN: 978-989-758-688-0; ISSN: 2184-4305

The feature dimensionality reduction and SMOTE

techniques increased prediction accuracy; however,

they were unable to accurately predict the severity of

heart disease. Previous studies have adopted a one-

time classification, i.e., targetting all the data

simultaneously to classify severity levels from 0

(non-heart disease) to 4 (the highest severity of heart

disease), such as the work done by Abdellatif et al.

(Abdellatif et al., 2022) that adopted an extra tree.

Our intuition is that the features to classify heart-

disease severity levels differ depending on levels 0 to

4, which leads to the idea that relevant features are

better adapted depending on which severity levels to

classify. Therefore, a hierarchical binary

classification (HBC) is adopted, starting the

classifications from the lowest severity (large number

of patients) to the highest severity (small number of

patients). This classification technique also

beneficially makes the binary classification for

imbalanced data closer to balanced data. That is, the

first prediction targets the classification between level

0 and the rest (levels 1 to 4), followed by level 1 and

the rest (levels 2 to 4), then level 2 and the rest (levels

3 to 4), and finally level 3 and 4. This approach allows

for the adoption of relevant features in each

prediction. In addition, medical knowledge and

statistical information are adopted to decrease the

feature space to increase the prediction accuracy.

Specifically, the continuous values are converted to

binary data in the data-preprocessing step,

contributing to the efficient learning of the model.

mRMR is also adopted as a dimensionality-

reduction method owing to its exceptional

performance (Wang et al., 2022). Additionally,

SMOTE is adopted to balance the dataset for

prediction, as in the work of Lakshmi et al. (Lakshmi

and Devi, 2023).

Our contributions are summarized below:

1) A novel classification method is proposed to

predict the severity of heart disease in

cooperation with HBC to maximize the use of

features that characterize each severity level.

2) Medical knowledge and statistical information

are adopted to decrease the feature space.

3) Experimental evaluation using the heart-disease

dataset from the UCI machine-learning

repository confirmed the highest severity level

prediction accuracy of 93.13%. When specialized

in the Cleveland dataset, the proposed approach

achieved an accuracy of 96.67% compared with

that of a state-of-the-art method (95.73%).

The remainder of this paper is organized as

follows. The related work is described in Section 2

and the dataset used in this study is presented in

Section 3. The methodology is presented in Section 4,

followed by an experimental evaluation in Section 5.

Finally, the study is concluded in Section 6.

2 RELATED WORK

The heart-disease dataset provided by the UCI

machine-learning repository was originally published

by Janosi et al. in 1988, which is the most commonly

used dataset on heart-disease prediction. The

prediction targets determine the existence and the

severity level of heart disease. Recent studies

utilizing the UCI dataset are summarized in Table 1.

The first eight studies predicted the presence of

heart disease. Akgül et al. (Akgül, Sönmez, and

Özcan, 2020) proposed combining an artificial neural

network (ANN) with a genetic algorithm. Gupta et al.

(Gupta et al., 2020) proposed a feature selection

method for data in which quantitative and qualitative

variables were mixed through a factor analysis of

mixed data (FAMD). Lohumi et al. (Lohumi et al.,

2020) used normalization for data preprocessing and

found that the ensemble learning of random forests

(RFs) achieved better accuracy than those of other

ensemble learning methods. Xiao et al. (Xiao et al.,

2020) proposed the use of a deep-residual neural

network and found it to be superior to conventional

machine-learning methods. Rao et al. (Rao, Gopal,

and Lata, 2021) found that each data from four

locations had a distinct suitable model to predict.

Balamurugan et al. (Balamurugan et al., 2022) found

that using an enhanced deep-genetic algorithm as the

classification method and stochastic gradient

boosting-recursive feature elimination (SGB-RFE) as

the feature selection method improved accuracy.

Pratama et al. (Pratama et al., 2022) proposed using

the F-score for feature selection and gradient tree

boosting for classification.

The state-of-the-art method (Wang et al., 2022)

achieved 100%, 98.3%, and 99.0% accuracy in

predicting the presence of heart disease in the

Cleveland, Hungarian, and Long-Beach-VA datasets,

respectively. They pointed out that the high

dimensionality of features prevented the

improvement of accuracy. Therefore, they compared

five types of dimensionality reduction methods:

Principal Component Analysis, Linear Discriminant

Analysis, Kendall, RF, and mRMR. Then, they

concluded that mRMR was the best.

The remaining seven studies predicted the

severity of heart disease. Amin et al. (Amin, Chiam,

and Varathan, 2019) proposed Vote, which used the

identified significant features and a best-performing

data-mining technique. Mohan et al. (Mohan,

Thirumalai, and Srivastava, 2019) proposed an RF

Prediction of Heart Disease Severity Using Hierarchically-Structured Machine-Learning Models with Feature Space Reduction

663

Table 1: Research on the prediction of heart disease presence and severity.

Reference Dataset Prediction Feature Selection Classifie

Accuracy

(

)

(

ül et al., 2020

)

Clevelan

Presence None ANN-GA 95.82

(

ta et al., 2020

)

Clevelan

Presence FAMD RF 93.44

(

Lohumi et al, 2020

)

Clevelan

Presence None RF

(

Ensemble

)

96.26

(Xiao et al., 2020) Clevelan

Presence None Deep Recurrent

Neural Networ

95.00

(Rao et al., 2021) Clevelan

Hungarian

Long-Beach-VA

Switzerland

Presence None

-nearest

neighbors

(kNN), XGBoost

(XGB), and

AdaBoost

(

)

86.81

84.26

82.20

98.00

(Balamurugan et al., 2022) Clevelan

Presence SGB-RFE Enhanced Deep

Genetic

orith

98.36

(

Pratama et al., 2022

)

Clevelan

Presence F-score GTB 99.00

(Wang et al., 2022) Cleveland

Hungarian

Lon

-Beach-VA

Presence mRMR RF, ANN, GB,

and SVM

100.0

98.3

99.0

(

Abdellatif et al.,2022

)

Clevelan

Severit

None ET 95.73

(Amin et al., 2019) Clevelan

Severity Brute-force

Vote 87.41

(

Mohan et al., 2019

)

Clevelan

Severit

DT entro

HRFLM 88.70

(

Kibria and Matin, 2022

)

Clevelan

Severit

None LR+RF 75.41

(Yuan et al., 2022) Clevelan

Hungarian

Long-Beach-VA

Switzerlan

Severity None Bagging-Fuzzy-

GBDT

90.00

(for all datasets)

(Satyanan

am and

Sat

anara

ana, 2021

)

Clevelan

Severity None RF 81.99

(Lakshmi and Devi, 2023) Cleveland,

Hungarian

Long-Beach-VA

Switzerlan

Severity Genetic

Algorithm

SVM 82.00

(for all datasets)

with Linear hybrid model (HRFLM) with decision

tree (DT) entropy to select features. Kibria and Matin

(Kibria and Matin, 2022) used an ensemble model

with logistic regression (LR) and RF. Yuan et al.

(Yuan et al., 2022) proposed a begging-fuzzy gradient

boosting DT (GBDT) as the classifier. Satyanandam

and Satyanarayana (Satyanandam and

Satyanarayana, 2021) proposed using polynomial

regression. Lakshmi and Devi (Lakshmi and Devi,

2023) used an SVM after future selection by a genetic

algorithm.

A state-of-the-art method adopting extra trees

(ET) was proposed by Abdellatif et al. (Abdellatif et

al., 2022), which achieved 95.73% accuracy in

classifying patients into five heart-disease severities

using the Cleveland dataset.

Although the presence prediction showed a high

accuracy (close to 100%), the accuracy of the severity

prediction was low (see Table 1) and could not be

used for diagnosis. Thus, the accuracy of predicting

heart disease severity is indispensable to improve.

3 DATASETS

The UCI machine-learning repository provides the

most commonly used heart-disease dataset and

comprises 76 patient features from four locations, as

shown in Table 2.

Table 2: Heart-disease dataset from the UCI machine-

learning repository. (UC Irvine Machine Learning

Repository, 1988).

Location

Dataset

#of participants

Severity level (0:no-heart

disease, 1:low, 4:high)

Total

0 1 2 3 4

Cleveland 164 55 36 35 13 303

Hungarian 188 37 26 28 15 294

Long-Beach-VA 51 56 41 42 10 200

Switzerland 8 48 32 30 5 123

Total 411 196 135 135 43 920

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4 PROPOSED METHOD

Figure 1 provides an overview of the proposed steps

for predicting the severity of heart disease. The main

contributions of this study are discussed in Sections

4.2 and 4.5, where Section 4.2 proposes a new data

preprocessing technique to decrease the feature space,

and Section 4.5 proposes a hierarchically-structured

machine-learning model, named hierarchical binary

classification (HBC).

Figure 1: Proposed method.

4.1 Missing Value Completion

Multiple imputation by chained equation (MICE),

known as switching or sequential regression

multivariate imputation, was applied to impute

missing values for each feature, similar to the work of

Rani et al. (Rani, Kumar, and Jain, 2021). We used

the R package of MICE (3.16.0), in which the number

of pseudo-data was set to 10 and predictive mean

matching imputation was used. MICE was not

applied if over 90% of the columns for a single feature

contained the default missing value of -9. Instead, the

columns were filled with the default value of -9 owing

to insufficient information to apply MICE.

4.2 Data Preprocessing

We propose a new data preprocessing technique,

named PRE, to eliminate outliers and decrease feature

ranges. The continuous values of each feature are

reduced to the binary values of 0 and 1 by using

medical knowledge and statistical information, where

the values in the range of heart-disease susceptibility

to 1 and the values in the range of heart disease non-

susceptibility to 0.

First, the medical knowledge for age, cigarettes

per day (cigs), and years as a smoker (years) were

adopted. People aged 65 years and above are more

susceptible to heart disease (National Center for

Health Statistics, 2023). Consequently, the age

feature was replaced with 1 if the age was 65 years

and above; otherwise, it was replaced with 0.

According to the National Heart, Lung, and Blood

Institute (National Heart, Lung, and Blood Institute,

2022), smoking harms blood vessels and the heart;

thus, each value of cigs and years was set to 1 if the

value was over 0; otherwise, it was set to 0.

Second, the remaining features were analyzed to

determine whether continuous values could be

replaced with binary values, contributing to reducing

the feature space. Algorithm 1 represents the detailed

steps. In Algorithm 1, an optimal threshold value is

selected to replace the continuous value with 0 or 1.

We hypothesize that the continuous value is

related to the target label to classify (severity level).

In other words, we hypothesize that if the continuous

values are split into the first group and the second

group using some appropriate threshold value, the

first group tends to contain the data of label-A, e.g., a

lower severity level, and the second group contains

the data of label-B, e.g., higher severity level. If an

appropriate threshold value is selected, there should

be a large difference between the distribution of the

labels in the first and second groups. Thus, we use p-

values to confirm the existence of a large difference

in the distribution between label-A and label-B,

where label-A is represented by 0 and label-B is

represented by 1. In addition, we confirm the

distribution of continuous values between label-A

and label-B in the same way. If the p-value of binary

values is smaller than that of continuous values, we

adopted the threshold value that yields the smallest p-

value (<0.05). Note that if no threshold satisfies

p<0.05, we continue using the continuous values.

Input: cv

// continuous values larger than 0 (1<=i<=N).

Output: optimal_th // an optimal threshold value to split

the continuous values into binary; -1 represents no splitting,

i.e., the continuous values should be used as they are.

min_p = 1; // initialization of a temporal value

optimal_ph=-1; // initialization of a temporal value

min_cv = minimum(cv

) for all i, where 1<=i<=N;

max_cv = maximum(cv

) for all i, where 1<=i<=N;

for th=min_cv to max_cv // candidate threshold value

1st_group={sl

| sl

=label of cv

where cv

<=th};

2nd_group={sl

| sl

=label of cv

where cv

>th};

// labels are represented by binaries (0 and 1).

p-value is calculated between 1st and 2nd groups;

if ((p_value<min_p) AND (p_value < 0.05)) then

min_p = p_value; // update minimum p-value

optimal_th=th; // update an optimal th

end if

end for

return optimal_th;

Algorithm 1: Optimal threshold calculation.

Prediction of Heart Disease Severity Using Hierarchically-Structured Machine-Learning Models with Feature Space Reduction

665

4.3 Dimensionality Reduction

mRMR was used for dimensionality reduction. The

number of features was set to 8, 10, 12, 14, 16, 18,

and 20 to compare the accuracies. mRMR was

performed for each severity level and location,

followed by selecting the best features.

4.4 Oversampling

The datasets were imbalanced, as shown in Table 2.

Similar to the work done by Abdellatif et al.

(Abdellatif et al., 2022), SMOTE was adopted to

balance the amount of data in each training.

Oversampling using SMOTE was conducted on the

training data to balance the number of data when

conducting 10-fold cross-validation. The SMOTE

parameter, random_state, was set as 42.

4.5 Hierarchical Binary Classification

A hierarchically-structured machine-learning model

was proposed for HBC, as shown in Figure 2. The

proposed model predicts severity levels using

features fitted to each severity level. Previous studies,

such as that of Lakshmi and Devi (Lakshmi and Devi,

2023), used a one-time classification technique. For

example, their study classified severity levels from 0

to 4, targeting all data simultaneously. Thus, they

could not tune the features to classify targeting at each

severity level. This led to the idea that relevant

features could be better adapted depending on the

severity level.

Here, the circled numbers indicate the predicted

results of the severity levels. The classification

process was as follows:

1) All data were divided into non-heart disease

(0) and heart disease (1 to 4).

2) The heart disease data classified in Step 1 were

divided into level 1 and above (2 to 4).

3) The heart disease data were divided into level

2 and above (3 to 4).

4) The heart disease data were divided into levels

3 and 4.

The proposed method was expected to be more

precise than the previous approach that

simultaneously divides the severity levels from 0 to 4.

This is because each level may have different relevant

features for prediction. Furthermore, another

advantage of the proposed method is that a nearly

balanced dataset can be prepared at each step.

The scikit-learn library version 1.0.2

(https://scikit-learn.org/stable/) was used to adopt the

following seven classic machine-learning methods to

select the best-performing one: Logistic Regression

(LR), Decision Tree Classifier (DT), AdaBoost (AB),

Random Forest (RF), K-Nearest-Neighbor (kNN),

XGBoost (XGB), and Support Vector Machine

(SVM). It should be noted that a 10-fold cross-

validation method was adopted, and parameter tuning

was performed for each model.

Figure 2: The proposed hierarchical binary classification.

5 EVALUATION

Experiments were conducted to confirm whether the

proposed method improved accuracy.

5.1 Metrics

The accuracy, mean absolute error (MAE), and

maximum absolute error (MAXAE) were adopted as

evaluation metrics. The MAE shows the extent to

which the predicted severity level differs from the

actual level, which is expressed as:

 

∑|











(1)

where 



denotes the predicted level, 



denotes

the actual level, and  denotes the number of data.

The MAXAE shows how the maximum value of the

predicted severity level differs from the actual level,

which is expressed as:

  max

|









|



(2)

5.2 Baseline

The results of the baseline are listed in Table 3. The

number of features in Table 3 indicates the best

number of features chosen from 8, 10, 12, 14, 16, 18,

and 20. The model column in Table 3 lists the model

that achieved the best accuracy among the LR, DT,

AB, RF, kNN, XGB, and SVM.

As shown in Table 3, a small number of features

resulted in the best accuracy, and the best machine-

learning model varied for individual datasets. Besides,

the precision varied for the levels owing to the

number of patients and characteristics of the data,

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666

although the accuracy of non-heart disease (level 0)

was high in all locations.

5.3 Proposed Methods

PRE Method. The results of the proposed PRE

method are listed in Table 4. By comparing Tables 3

and 4, the proposed PRE method was confirmed to be

more accurate in three locations, where the accuracy

increased and the MAE decreased. However, the

accuracy decreased in Switzerland while the MAE

increased because the amount of data before

oversampling in Switzerland was smaller than in

other locations. Hence, the distribution of features

was different in the training and test data, and some

features were no longer relevant to the diagnosis of

the test data when converted to binary data.

HBC Method. The results of the proposed HBC

method are listed in Table 5. By comparing Tables 3

and 5, it was confirmed that the accuracy improved

and the MAE decreased at all locations. In addition,

the MAXAE was decreased at 3 locations, indicating

that the predicted rank was closer to the actual rank

than in the baseline. The trend in the number of

features was that a higher number of features

provided better accuracy when predicting (1) vs. (2

–

4) and (2) vs. (3–4). However, a smaller number of

features provided better accuracy when predicting (0)

vs. (1

–4) and (3) vs. (4). This was owing to the similar

trends in the values of levels (1), (2), and (3). A

comparison of Tables 3 and 5 shows that the precision

improved at all levels.

HBC+PRE Method. The results of the proposed

HBC+PRE method are listed in Table 6. Compared

with the results of the other methods, combining the

two proposed techniques exhibited the best results

because the accuracy and MAE values were the best.

The precision for each level was also the best

compared to the other methods.

5.4 Summary

The experimental results for severity and presence

prediction are summarized in Tables 7 and 8,

respectively. The proposed HBC+PRE method

achieved the best accuracy for all datasets. The MAE

and MAXAE confirmed that HBC successfully

narrowed down the errors of predicted levels.

Table 3: Results of the baseline.

Location of

Dataset

# of

features

Model Accuracy MAE MAXAE Precision of each severity level

0 1 2 3 4

Cleveland 8 LR 81.43 0.200 2 100.00 87.17 28.00 66.33 100.00

Hungarian 14 XGB 67.92 0.513 4 86.69 29.00 32.50 51.17 11.67

Long-Beach-VA 8 LR 79.41 0.282 4 98.00 93.33 53.83 72.00 65.00

Switzerland 10 DT 89.09 0.091 1 100.00 90.00 91.67 88.33 100.00

Table 4: Results of the proposed PRE method.

Location of

Dataset

# of

features

Model Accuracy MAE MAXAE Precision of each severity level

0 1 2 3 4

Cleveland 8 DT 86.19 0.148 2 94.84 89.17 45.67 75.00 100.00

Hungarian 12 XGB 69.17 0.433 4 91.40 29.08 19.17 56.50 20.00

Long-Beach-VA 8 DT 86.47 0.188 3 94.67 93.00 76.17 88.17 35.00

Switzerland 14 DT 85.46 0.164 2 90.00 80.00 89.17 88.33 100.00

Prediction of Heart Disease Severity Using Hierarchically-Structured Machine-Learning Models with Feature Space Reduction

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Table 5: Results of the proposed HBC method.

Location of

Dataset

Levels to

Classify

# of

Feature

Model

Accuracy

MAE

MAXAE

Precision of each severity level

0 1 2 3 4

Clevelan

0, 1-4 8 LR

91.90 0.081 1 100.0 90.17 57.50 88.33 100.0

1, 2-4 20 LR

2, 3-4 14 LR

3, 4 8 DT

Hungarian 0, 1-4 10 SVM

87.92 0.133 3 100.0 78.33 54.00 78.50 40.00

1, 2-4 12 LR

2, 3-4 18 LR

3, 4 18 kNN

Long-

Beach-VA

0, 1-4 16 DT

88.24 0.118 1 100.0 93.33 69.17 87.50 100.0

1, 2-4 12 LR

2, 3-4 8 LR

3, 4 8 DT

Switzerlan

0, 1-4 8 kNN

89.09 0.127 2 100.0 94.67 90.83 80.00 90.00

1, 2-4 10 AB

2, 3-4 12 AB

3, 4 8 LR

Table 6: Results of the proposed HBC+PRE method.

Location of

Dataset

Levels to

Classify

# of

Feature

Model

Accuracy

MAE

MAXAE

Precision of each severit

level

0 1 2 3 4

Clevelan

0, 1-4 8 DT 96.67 0.043 1 100.0 96.00 80.00 95.00 100.0

1, 2-4 20 XGB

2, 3-4 14 LR

3, 4 8 LR

Hungarian 0, 1-4 10 SVM 91.25 0.096 2 100.0 86.00 55.67 84.17 70.00

1, 2-4 8 AB

2, 3-4 16 LR

3, 4 16 LR

Long-

Beach-VA

0, 1-4 16 AB 90.59 0.094 1 100.0 96.67 75.83 91.00 100.0

1, 2-4 8 LR

2, 3-4 14 SVM

3, 4 8 LR

Switzerlan

0, 1-4 12 AB 93.64 0.091 3 100.0 96.00 95.00 90.00 90.00

1, 2-4 18 AB

2, 3-4 12 AB

3, 4 8 LR

Table 7: Summary of results for severity prediction. (An asterisk (*) in the accuracy column indicates a statistically significant

difference (p<0.05) compared with the baseline after paired t-tests).

Location of Dataset Accuracy Precision Recall F

MCC MAE MAXAE

Clevelan

Baseline 81.43 87.49 81.43 81.67 0.723 0.200 2

HBC+PRE 96.67* 96.86 96.67 96.44 0.950 0.043 1

(Abdellatif et

al.,2022

)

95.73 96.35 95.73 95.78 0.934 N/A N/A

Hungarian Baseline 67.92 67.39 67.92 66.17 0.442 0.513 4

HBC+PRE 91.25* 91.33 91.25 90.45 0.851 0.096 2

Long-

Beach-VA

Baseline 79.41 80.77 79.41 78.01 0.746 0.282 4

HBC+PRE 90.59* 92.64 90.59 90.67 0.884 0.094 1

Switzerlan

Baseline 89.09 91.33 89.09 88.92 0.862 0.091 1

HBC+PRE 93.64 94.45 93.64 93.02 0.921 0.091 3

All Baseline 77.61 79.95 77.61 76.78 0.655 0.306 4

HBC+PRE 93.13 93.78 93.13 92.73 0.899 0.078 3

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Table 8: Summary of accuracy results (Presence).

Location of

Dataset

Baseline PRE

osed

)

HBC

osed

)

HBC+PRE

osed

)

Wang et al. (Wang et al.,

2022

)

Cleveland 98.09 95.71 100.00 100.00 100.00

Hungarian 82.92 85.00 100.00 100.00 98.30

Long-Beach-VA 98.82 99.41 100.00 100.00 99.00

Switzerland 100.00 96.36 100.00 100.00 N/A

6 CONCLUSION

Heart disease is a global cause of death and its

prediction can contribute to its early diagnosis. This

study assessed the severity of heart disease using two

proposed techniques to increase the prediction

accuracy: 1) an HBC technique to classify severity in

order from the lowest level, and 2) a PRE technique

to transform continuous values into binary values

based on whether the values were likely to cause heart

disease. Using 10-fold cross-validation, the proposed

method achieved 96.67%, 91.25%, 90.59%, and

93.64% accuracy for severity prediction in the

Cleveland, Hungarian, Long-Beach-VA, and

Switzerland datasets, respectively, which were the

highest accuracies compared with previous research.

Our proposed method also achieved the smallest

mean absolute error (MAE). It is worth noting that the

accuracy of predicting the presence or absence of

heart disease was 100% in all datasets. The results of

this study can be used to improve the early detection

and treatment of heart disease and ultimately reduce

the number of deaths in the future.

ACKNOWLEDGMENTS

A part of this work was supported by JSPS

KAKENHI, Grant-in-Aid for Scientific Research

(A), Grant Number 22H00067.

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