Predictive model for acute myocardial infarction in working-age population: a machine learning approach

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 1, February 2024, pp. 854~860
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp854-860  854
Journal homepage: http://ijece.iaescore.com
Predictive model for acute myocardial infarction in
working-age population: a machine learning approach
Astrid Lorena Urbano-Cano1
, Diana Jimena López-Mesa2
, Rosa Elvira Alvarez-Rosero3
,
Yeison Alberto Garcés-Gómez4
1
Department of Biology, Faculty of Natural Sciences and Education, Universidad del Cauca, Popayán, Colombia
2
Faculty of Engineering, Corporación Universitaria Comfacauca, Popayán, Colombia
3
Department of Physiological Sciences, Faculty of Health Sciences, University of Cauca, Popayán, Colombia
4
Faculty of Engineering and Anchitecture, Universidad Católica de Manizales, Manizales, Colombia
Article Info ABSTRACT
Article history:
Received Jul 9, 2023
Revised Jul 4, 2023
Accepted Jul 17, 2023
Cardiovascular diseases are the leading cause of mortality in Latin America,
particularly acute myocardial infarction (AMI), which is the primary cause
of atherosclerotic cardiovascular morbidity. This study aims to develop a
predictive model for the probability of AMI occurrence in the working-age
population, based on atherogenic indices, paraclinical variables, and
anthropometric measures. The research conducted a cross-sectional study
involving 427 workers aged 40 years or older in Popayán, Colombia. Out of
this population, 202 individuals were screened with a 95% confidence
interval and a 5% error margin. Epidemiological, anthropometric, and
paraclinical data were collected. A binary logistic regression model was
employed to identify variables directly associated with the probability of
AMI. Predictive classification models were generated using statistical
software JASP and the programming language Python. During the training
stage, JASP produced a model with an accuracy of 87.5%, while Python
generated a model with an accuracy of 90.2%. In the validation stage, JASP
achieved an accuracy of 93%, and Python reached 95%. These results
establish an effective model for predicting the probability of AMI in the
working population.
Keywords:
Cardiovascular risk
Machine learning
Myocardial infarction
Prediction model
Random forest
This is an open access article under the CC BY-SA license.
Corresponding Author:
Yeison Alberto Garcés-Gómez
Department of Electrical Engineering, Faculty of Engineering and Architecture, Universidad Católica de
Manizales
Cra 23 No 60-63, Manizales, Colombia
Email: ygarces@ucm.edu.co
1. INTRODUCTION
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide, affecting
millions of people every year. CVD is largely preceded by atherosclerosis, which is a major precursor to the
three most common vascular diseases: acute myocardial infarction (AMI), stroke, and peripheral arterial
disease (PAD). Together, these diseases represent approximately one-third of all deaths worldwide [1], [2].
The AMI is defined as a necrosis in a clinical setting compatible with acute myocardial ischemia,
which is commonly secondary to thrombotic occlusion of the coronary artery. Pain is a dominant
characteristic symptom of this condition. It should be noted that the AMI is also based on the presence of
myocardial damage detected by the elevation of cardiac biomarkers in the context of evidence of acute
myocardial ischemia [3]. The AMI contributes more than 80% of cases of ischemic heart disease due to
atherosclerosis, being more frequent in men than in women. Additional risk factors that can cause AMI

Int J Elec & Comp Eng ISSN: 2088-8708 
Predictive model for acute myocardial infarction in working-age … (Astrid Lorena Urbano-Cano)
855
include obesity, a high-calorie diet, smoking, type 2 diabetes mellitus, hypertension, and dyslipidemia,
among others [4]. Atherosclerotic risk can be predicted by measuring atherogenic indices based on the lipid
profile, which consist of the mathematical ratio or proportion between the levels of total cholesterol,
triglycerides, high-density lipoprotein (HDL), or low-density lipoprotein (LDL) [5]. However, it is important
to note that these indices are not explored in the working population of the region, which is necessary for the
economic development of the territory. Therefore, it is necessary to include the work environment in this
study to obtain more accurate results on the risk of developing AMI.
It is important to highlight that people with this pathology or a history of it have significantly
reduced quality and life expectancy due to premature deaths and years of life lost due to disability. This
represents a high health cost. Specifically, in individuals of working age, significant economic burdens are
evidenced due to work disability, either due to absenteeism or loss of productivity. Therefore, it is necessary
to create mechanisms that allow predicting the probability of occurrence of AMI in working individuals to
create strategies that mitigate its effects within the work environment [6].
The use of autonomous learning algorithms or machine learning algorithms applied to the field of
medicine is a novel topic. Currently, several studies in the literature report the use of algorithms such as
artificial neural networks, case-based reasoning, Bayesian networks, decision trees, or k-means to support the
diagnosis of diseases such as breast cancer, prostate cancer, cardiovascular diseases, hypertension,
Parkinson's, infarctions, rheumatoid arthritis, among others, and the prediction of mortality or survival after
cardiovascular events [7]. The use of these techniques becomes a fundamental support for healthcare
personnel since some pathologies could be prevented and adequately treated before they present major
complications, improving the survival chances of patients. Its application is also carried out in the emergency
area of hospitals when diagnosing patient triage, reducing assessment times, and assigning the correct care
shift [8]. Several studies related to this topic have been summarized in this document.
For pancreatic cancer, the study by [9] used neural networks to predict individual long-term survival
of patients undergoing radical surgery for this type of cancer, with a performance of 79%. On the other hand,
the research by [10] exposed the application of neural networks to recognize complex patterns for the
prediction of advanced bladder cancer in patients undergoing radical cystectomy, breast cancer, and also
prediction of survival after hepatic resection for colorectal cancer. The implemented model resulted in a
disease prediction rate of 90.5% and individual survival prediction of 72% [11]–[13].
The research by [14] shows how classification algorithms such as logistic model tree (LMT),
Bayesian networks, naive Bayes, J48, and naive Bayes simple were used for the diagnosis of pathologies in
the Spine, to decide which is the best algorithm for the diagnosis of this disease. The results obtained during
classification show that the LMT decision algorithm obtained a success rate of 85.48%. The Bayesian
networks algorithm had a success rate of 80%; the naive Bayes algorithm correctly classified 248 instances
with an absolute error of 2%, and finally, the naive Bayes simple algorithm correctly classified 241 instances
of the 310, reaching the conclusion that the best decision algorithm is the LMT.
In [15], important results were obtained for the diagnosis of rheumatoid arthritis, as well as its
categorization and potential application in personalized medicine for individuals affected by this disease.
Computational models were designed for classification, among which are artificial neural networks that using
5 variables obtained a sensitivity of 92.3% with a specificity of 86.66%, and with Bayesian networks, a
sensitivity of 92.3% and a specificity of 93.33% were achieved. Using artificial neural networks in [16], a
model capable of recognizing 3 types of values: cirrhosis, non-cirrhosis, and non-identifiable with a success
rate of almost 90% was obtained.
For the prognosis of bladder cancer mortality, [17] used seven learning methods to predict mortality
at five years after radical cystectomy, including neural networks, radial basis function networks, extreme
learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), and the nearest
neighbor classifier (K-NN). The results indicate that RELM achieves the highest prediction accuracy with
80%. In patients with stroke (ischemia and hemorrhage), for the prognosis of mortality 10 days after the
event, the research by [18] applied neural networks to obtain a predictive model and achieved a sensitivity
and accuracy of 87.8% for the hemorrhagic group and specificity of 75.9%, sensitivity of 85.9%, and
accuracy of 80.9% for the ischemic group.
On the other hand, [19] show the training and testing of different neural networks for the diagnosis
of myocardial infarction. The training and testing of several neural networks with different architectures were
carried out for the diagnosis of infarction, based on the data from the Braunwald angina probability rating
scale [20]. 40 networks were generated and tested in 5 experiments, of which the diagnostic accuracy was
higher with the model of 5 electrocardiographic inputs plus troponin. Several of the networks designed for
this case had a sensitivity and specificity close to 99%. In turn, [21] in their research expose different
machine learning algorithms for the classification of breast cysts through thermographic images using
artificial neural networks. Their results indicate a sensitivity of 78% and specificity of 88%. The overall
efficiency of the system was 83%

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In other areas of the medical field, artificial neural networks have been proposed to predict
prolonged hospital stay for elderly patients in the emergency department, as well as prolonged stay in the
intensive care unit and mortality, achieving a sensitivity of 62.5% and specificity of 96.6% for hospital stay
and 82% for mortality prediction [22]–[24]. Previous research has shown that the use of autonomous learning
algorithms is an effective tool for the diagnosis and prediction of disease onset, allowing the medical team to
provide timely treatment and thus improving the patient's quality of life. Within the literature of the last five
years, consulted by the authors of this study, there is no evidence of predictive models focused specifically
on the probability of suffering from AMI related to atherogenic indices, anthropometric measures or
paraclinical variables for the working population.
2. METHOD
The study is an observational, cross-sectional descriptive study, whose database started to be
analyzed from January 2022. A cross-sectional study was conducted in 427 workers aged ≥40 years in the
city of Popayán, from which 202 individuals were screened, considering a confidence interval of 95% and
an error of 5%. Subsequently, epidemiological, clinical, and paraclinical data were collected, the latter
from a peripheral blood sample after obtaining informed consent. All the questionnaires, procedures, and
protocols were reviewed and approved by the Ethics Committee for Scientific Research at the University
of Cauca; the guidelines used in their view were based on the bioethical principles established in the
Helsinki in 1975 declaration and the parameters outlined in Resolution 8430 of the Colombian Ministry of
Health in 1993.
Atherogenic indices (AI) were calculated: total-cholesterol-high-density-lipoprotein ratio
(TC/HDL), low-density-lipoprotein-high-density-lipoprotein ratio (LDL/HDL), TC-HDL/HDL, TC-HDL,
logarithmic triglycerides-to-high-density-lipoprotein (LOG(TG/HDL)), and TG/HDL. Cardiovascular risk
was measured using the Framingham scale, using variables such as age, sex, systolic blood pressure,
diabetes, smoker, total cholesterol levels, and HDL. Later, a correlation will be made between the
atherogenic indices and the percentage of cardiovascular risk, estimating the risk of developing
atherosclerosis [5] and the coronary risk according to the Framingham adjusted function, recommended by
the Colombian guide [24]. The Framingham-adjusted scale (Framingham cardiovascular risk × 0.75)
proposed recently by the clinical practice guide for the prevention, early detection, diagnosis, treatment, and
follow-up of Dyslipidemia in Colombia [25], [26] will be used.
According to the purposes of this study, the occurrence of AMI was taken as the dependent variable,
which is categorical and binary, with output values of "yes" or "no." The remaining variables that may be
related to it are quantitative (cholesterol, triglycerides, glucose, HDL, LDL, very-low-density lipoprotein
(VLDL), blood pressure, peripheral arterial disease (PAD), body mass index (BMI), abdominal perimeter
(AP), age, ICI_CT/cHDL, cholesterol-high-density-lipoprotein CHOL-HDL/HDL) and some are categorical
(sex, smoking, physical activity, marital status, education, race, origin, occupation, type of contract).
The analysis begins by determining the relationship between the different variables, so a logistic
regression is performed given the nature of the dependent variable. Different combinations of variables are
performed using the JASP statistical software until the most favorable result is obtained, where a relationship
was found between the occurrence of acute myocardial infarction and the variables summarized in Table 1.
Table 1. The performance of risk variables related to AIM
Variable type Name Range or value
Quantitative Cholesterol (117 – 300) mg/dL
Triglycerides (88 – 424,7) mg/dL
Blood glucose (68 – 310) mg/dL
BMI (18 – 45,7) Kg/cm2
AP (42 – 162) cm
PAD (0,72 – 1,25)
ICI_CT/cHDL (2 – 16,22)
CHOL-HDL/HDL (1 – 15,22)
Categorical Sex M-F
3. RESULTS AND DISCUSSION
3.1. Logistic regression
According to the Akaike information criteria (AIC) and Bayesian information criteria (BIC) metrics,
the alternative hypothesis (H1) has the lowest values, suggesting a significant relationship between the output
and predictor variables as shown in Table 2. The McFadden R2 value is 0.339, indicating that it is a good

857
model as its value falls within the range of 0.2-0.4. The p-value is 0.001, which, being less than 0.05, shows
the good performance of the model. The Nagelkerke, Tjur; and Cox and Snell R2 values are very useful when
comparing different models for the same data, where the model with the highest R2 values is considered the
most appropriate. However, this is not the purpose of this analysis [27].
Table 2. The performance of logistic regression
Model Deviance AIC BIC df X2 P McFadden R2 Nagelkerke R2 Tjur R2 Cox &Snell R2
H0 278,425 280,425 283,734 201
H1 184,031 204,031 237,113 192 94,395 <.001 0.339 0.499 0.477 0.373
According to the confusion matrix as shown in Table 3, out of the 202 entered data, 104 data that
should have been classified as NO were correctly classified, and 77 that should have been classified as YES
were correctly classified, showing an accuracy of 92.8%. The sensitivity is 83.7% and the specificity is
94.5% as seen in Table 4. These results show a good performance of the algorithm that also eliminates
subjectivity and the need for highly trained medical personnel.
Table 3. The performance of confusion matrix obtained with logistics regression
Predicted
Observed no si % Correct
no 104 6 94,545
si 15 77 83,696
Overall % Correct 89,604
Table 4. The performance of metrics
Value
Sensitivity 0.837
Specificity 0.945
Precision 0.928
3.2. Machine learning algorithms
Given the results obtained in the logistic regression, it was possible to clearly identify the variables
that will be part of the predictive model that is intended to be generated. For the construction of the model,
the machine learning classification module is used, which proposes several methods, including boosting,
decision tree, k-nearest neighbors, random forest, support vector machine [27]. Comparing the different
metrics of the prediction methods related to machine learning classification, similarity between them is
evident. The average accuracy for boosting, decision tree, and random forest is 87.5%, and their precision is
88.1%, while for k-nearest neighbors and support vector machine, the average accuracy is 85%, and their
precision is 85.1%.
With these differences, although not significant, the selection is reduced to the methods: boosting,
random forest, and decision tree, where the first two are based on decision tree ensembles, which may have
an advantage over the decision tree technique, since according to the literature, this technique is not as robust,
as a small change in the data can cause large changes in the final estimated tree [28]. The boosting and
random forest methods differ in their training approach, since for random forest, each tree is trained
individually with a slightly different random sample of the training data generated by bootstrapping, while in
the boosting method, the trees are trained sequentially, so that each new tree tries to improve on the errors of
the previous trees [29]. Given that the results obtained are similar among the tree-based ensemble techniques
and due to its ease of interpretation, it was chosen to generate the predictive model using the random forest
technique.
3.3. Random forest algorithm
The database was inputted into JASP software and by using the random forest method of the
machine learning classification module, it was observed that with 14 trees each with 3 predictors per split and
taking 129 training data (64%), 33 validation data (16%), and 40 evaluation data (20%), an accuracy of
87.5% is obtained for both YES and NO values. As for precision, it is 92.9% for YES values and 84.6% for
NO values as shown in Tables 5 and 6, which represents very satisfactory results.

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Table 5. Summary of the classification model generated
Trees Predictors per split n(Train) n(Validation) n(Test) Validation accuracy Test accuracy OOB accuracy
14 3 129 33 40 0.909 0.875 0.893
Table 6. Confusion matrix of generated classification model
Predicted
no yes
Observed no 22 1
yes 4 13
Figure 1 shows the increase in accuracy for training data and validation data with respect to the
number of trees. From 14 trees onwards, the accuracy for validation is 90.9% and slightly lower for the
training data (87.5%). Finally, the ROC curves shown in Figure 2 indicate that both for the classification of
YES and NO, their shapes approach the desired one, with an area under the curve of AUC of 0.863, that is,
there is an 86.3% probability that the classification is correct.
Figure 1. Relationship of accuracy with respect to the
number of trees
Figure 2. ROC curves of the classification model of
the generated model
4. CONCLUSION
For the first time in the municipality of Popayan, a model has been established that allows predicting
the probability of suffering an acute myocardial infarction in a population over the fourth decade of life with
occupational activity. The mentioned model achieves a prediction accuracy of 95%. This result will help
reduce the incidence and mortality rates of the study population, improving their quality of life.
The predictive model was generated in two computational tools: the statistical software JASP and
the programming language Python, where the latter had better performance with a 2% difference in accuracy.
The obtained classification model showed satisfactory results in its accuracy, where in the training phase, this
value was 90.2% while in the validation phase, it increased to 95%. In the future, a cardiovascular risk
calculator could be generated to be used in a simple and non-invasive way in clinical practice.
ACKNOWLEDGEMENTS
Colfuturo for the financing of the project: Social determination of health in formal workers in the
city of Popayán: a territorial reading. Financed through call 002 Doctorate for National Teachers.
Specialization in Applied Statistics from the Catholic University of Manizales. Biology and Physiological
Science Departments of the University of Cauca. We are also indebted to all the volunteers who participated
in the study. Finally, we acknowledge the collaboration of the applied human genetics group of the
University of Cauca.
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BIOGRAPHIES OF AUTHORS
Astrid Lorena Urbano-Cano received the bachelor’s degree in biology from
Cauca University, Popayán, in 2003 and the M.S. and Ph.D. degrees in Biomedical Science
from Antioquia University and Valle University, Colombia, in 2011 and 2018, respectively.
Currently, she is an assistant Professor at the Department of Biology, Cauca University. The
research interests include genetic, statistics, cardiovascular disease and teaches several courses
such as genetics, molecular epidemiology, and investigation. She worked as researcher on
projects by the Ministry of Science, Tech and Innovation, Republic of Colombia. She can be
contacted at email: alurbano@unicauca.edu.co.
Diana Jimena López-Mesa received bachelor’s degree in industrial automatic
engineering and master’s degree in automatic engineering, from Electronic, Instrumentation
and Control Department, Universidad del Cauca, Popayán, Colombia, in 2009 and 2014,
respectively. She is Ph.D. student in electronic sciencies in Universidad del Cauca, and has
taught several courses such as electric machines, industrial process control, nonlinear control,
descriptive and inferential statistics, mathematical logic, software for industrial applications.
His main research focus is on electric drives, image processing, predictive models, and
machine learning techniques. E-mail: djlopez@unicauca.edu.co.
Rosa Elvira Alvarez-Rosero received the bachelor’s degree in biology from
Cauca University, Popayán, in 1996 and the M.S. and Ph.D. degrees in biomedical science and
environmental sciences from Valle University and Cauca University, Colombia, in 2013 and
2022, respectively. Currently, she is an Associate Professor at the Department of Physiological
Sciences, Cauca University. The research interests include genetic, enviromental,
cardiovascular disease and teaches several courses such as genetics and biology. She worked
as researcher on projects by the Ministry of Science, Tech and Innovation, Republic of
Colombia. She can be contacted at email: ralvares@unicauca.edu.co.
Yeison Alberto Garces-Gómez received bachelor’s degree in electronic
engineering, and master’s degrees and Ph.D. in engineering from Electrical, Electronic and
Computer Engineering Department, Universidad Nacional de Colombia, Manizales,
Colombia, in 2009, 2011 and 2015, respectively. He is a full Professor at the Academic Unit
for Training in Natural Sciences and Mathematics, Universidad Católica de Manizales, and
teaches several courses such as experimental design, statistics and physics. His main research
focus is on applied technologies, embedded systems, power electronics, power quality, but
also many other areas of electronics, signal processing and didactics. He published more than
30 scientific and research publications, among them more than 10 journal papers. He worked
as principal researcher on commercial projects and projects by the Ministry of Science, Tech
and Innovation, Republic of Colombia. He can be contacted at email: ygarces@ucm.edu.co.

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