Predictive model for the early detection of heart attacks in the population of Northern Peru
DOI:
https://doi.org/10.64439/cisai.v1i1.9Keywords:
Artificial neural networks, Predictive model, Myocardial infarction, Artificial intelligence, Public healthAbstract
Cardiovascular diseases represent one of the leading causes of mortality worldwide, and myocardial infarctions constitute one of the most critical manifestations of this problem. In the northern region of Peru, limited hospital infrastructure and insufficient access to specialized diagnostics increase the population’s vulnerability. This study proposes the development of a predictive model based on Artificial Neural Networks (ANN) for the early detection of heart attacks. Secondary data from the demographic and family health survey for the period 2005–2024 were used, comprising a total of 573,080 records that include risk factors such as hypertension, diabetes, obesity, smoking, and alcohol consumption. The methodology involved data cleaning, integration, and normalization, followed by the implementation of an ANN model in Python, validated using accuracy, sensitivity, and specificity metrics. Preliminary results show a predictive accuracy above 90%, supporting the feasibility of applying artificial intelligence in preventive medicine, particularly in scenarios characterized by high healthcare demand.
References
[1] O. S. Medina-Gómez, “Impacto de la pandemia de COVID-19 en las tendencias de mortalidad por enfermedades cardiovasculares en México, 2000-2022,” Medicina de Familia. SEMERGEN, vol. 50, no. 3, p. 102170, Apr. 2024. https://doi.org/10.1016/j.semerg.2023.102170
[2] H. G. Quezada-Pinedo et al., “A comprehensive analysis of cardiovascular mortality trends in Peru from 2017 to 2022: Insights from 183,386 deaths of the national death registry,” American Heart Journal Plus: Cardiology Research and Practice, vol. 35, p. 100335, Nov. 2023. https://doi.org/10.1016/j.ahjo.2023.100335
[3] J. A. Vázquez-Troche, V. García-Fernández, A. Hernández-Vásquez, R. Vargas-Fernández, and G. Bendezu-Quispe, “Trends in Mortality from Ischemic Heart Disease in Peru, 2005 to 2017,” IJERPH, vol. 19, no. 12, p. 7047, Jun. 2022. https://doi.org/10.3390/ijerph19127047
[4] M. A. Muzammil et al., “Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases,” Journal of Electrocardiology, vol. 83, pp. 30–40, Mar. 2024. https://doi.org/10.1016/j.jelectrocard.2024.01.006
[5] Gian Luigi Nicolosi, “Perché l’intelligenza artificiale applicata all’elettrocardiogramma non è ancora routine clinica?,” Recenti Progressi in Medicina, no. 2023Marzo, Mar. 2023. https://doi.org/10.1701/3981.39635
[6] I. Rojek, P. Kotlarz, M. Kozielski, M. Jagodziński, and Z. Królikowski, “Development of AI-Based Prediction of Heart Attack Risk as an Element of Preventive Medicine,” Electronics, vol. 13, no. 2, p. 272, Jan. 2024. https://doi.org/10.3390/electronics13020272
[7] A. Irsyad, P. P. Widagdo, P. P. Widagdo, and R. Wardhana, “Deep learning Methods for ECG-Based Heart Disease Detection,” j.electron.electromedical.eng.med.inform, vol. 6, no. 4, pp. 467–477, Sep. 2024. https://doi.org/10.35882/jeeemi.v6i4.498
[8] M. Mandava and S. Reddy Vinta, “MDensNet201-IDRSRNet: Efficient cardiovascular disease prediction system using hybrid deep learning,” Biomedical Signal Processing and Control, vol. 93, p. 106147, Jul. 2024. https://doi.org/10.1016/j.bspc.2024.106147
[9] J. Moya-Salazar et al., “Geospatial analysis of cardiovascular mortality before and during the COVID-19 pandemic in Peru: analysis of the national death registry to support emergency management in Peru,” Front. Cardiovasc. Med., vol. 11, p. 1316192, Jun. 2024. https://doi.org/10.3389/fcvm.2024.1316192
[10] L. M. Davila-Zamora et al., “Artificial Intelligence Adoption in Emerging Economies: Challenges, Opportunities, And Strategies for Peru’s Business Transformation,” Pak.j.life.soc.sci., vol. 22, no. 2, 2024. https://doi.org/10.57239/PJLSS-2024-22.2.000864
[11] U. R. Acharya, H. Fujita, S. L. Oh, Y. Hagiwara, J. H. Tan, and M. Adam, “Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals,” Information Sciences, vol. 415–416, pp. 190–198, Nov. 2017. https://doi.org/10.1016/j.ins.2017.06.027
[12] S. Gustafsson et al., “Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients,” Sci Rep, vol. 12, no. 1, p. 19615, Nov. 2022. https://doi.org/10.1038/s41598-022-24254-x
[13] D. Doudesis et al., “Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations,” Nat Med, vol. 29, no. 5, pp. 1201–1210, May 2023. https://doi.org/10.1038/s41591-023-02325-4
[14] B. Toprak et al., “Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study,” The Lancet Digital Health, vol. 6, no. 10, pp. e729–e738, Oct. 2024. https://doi.org/10.1016/S2589-7500(24)00191-2
[15] R. Khera et al., “Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction,” JAMA Cardiol, vol. 6, no. 6, p. 633, Jun. 2021. https://doi.org/10.1001/jamacardio.2021.0122
[16] H. Yu, Y. Qi, and Y. Ding, “Deep Learning in RNA Structure Studies,” Front. Mol. Biosci., vol. 9, p. 869601, May 2022. https://doi.org/10.3389/fmolb.2022.869601
[17] Z. Hu, Y. Li, and Z. Yang, “Improving Convolutional Neural Network Using Pseudo Derivative ReLU,” in 2018 5th International Conference on Systems and Informatics (ICSAI), Nanjing: IEEE, Nov. 2018, pp. 283–287. https://doi.org/10.1109/ICSAI.2018.8599372
[18] I. Lauriola, “On the Impact of Early Stopping in Multiple Kernel Learning,” in Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1, vol. 1288, K. Arai, S. Kapoor, and R. Bhatia, Eds., in Advances in Intelligent Systems and Computing, vol. 1288. , Cham: Springer International Publishing, 2021, pp. 205–215. https://doi.org/10.1007/978-3-030-63128-4_16
[19] D. J. Hand, P. Christen, and N. Kirielle, “F*: an interpretable transformation of the F-measure,” Mach Learn, vol. 110, no. 3, pp. 451–456, Mar. 2021. https://doi.org/10.1007/s10994-021-05964-1
[20] J. A. Martínez Pérez and P. S. Pérez Martin, “La curva ROC,” Medicina de Familia. SEMERGEN, vol. 49, no. 1, p. 101821, Jan. 2023. https://doi.org/10.1016/j.semerg.2022.101821
[21] J. T. Niedziela et al., “Is neural network better than logistic regression in death prediction in patients after ST-segment elevation myocardial infarction?,” Kardiol Pol, vol. 79, no. 12, pp. 1353–1361, Dec. 2021. https://doi.org/10.33963/KP.a2021.0142
[22] H. Silva Marchan, G. Ortiz Castro, O. J. M. Peña Cáceres, and M. A. More More, “Logistic regression: an example for the prediction of heart attacks,” in Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leadership in Education and Innovation in Engineering in the Framework of Global Transformations: Integration and Alliances for Integral Development,” Latin American and Caribbean Consortium of Engineering Institutions, 2023. https://doi.org/10.18687/LACCEI2023.1.1.720
[23] M. Casciaro et al., “Predictors of Acute Myocardial Infarction: A Machine Learning Analysis After a 7-Year Follow-Up,” Clinics and Practice, vol. 15, no. 4, p. 72, Mar. 2025. https://doi.org/10.3390/clinpract15040072
[24] A. Choi et al., “Development of Prediction Models for Acute Myocardial Infarction at Prehospital Stage with Machine Learning Based on a Nationwide Database,” JCDD, vol. 9, no. 12, p. 430, Dec. 2022. https://doi.org/10.3390/jcdd9120430
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Copyright (c) 2025 Henry Silva-Marchan, José Pardo Garces, Raúl Sánchez Ancajima, Gabriela Barrera Rea
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