Evaluation of Machine Learning Models for Predicting Cardiovascular Disease Based on Framingham Heart Study Data


Ruddy J Suhatril(1*); Rama Dian Syah(2); Matrissya Hermita(3); Bhakti Gunawan(4); Widya Silfianti(5);

(1) Universitas Gunadarma
(2) Universitas Gunadarma
(3) Universitas Gunadarma
(4) Universitas Gunadarma
(5) Universitas Gunadarma
(*) Corresponding Author

  

Abstract


The Framingham Heart Study Community is a research community that produces data related to Cardiovascular Disease (CVD). This research applies technology to predict CVD using machine learning based on data from the Framingham Heart Study. The eight machine learning algorithms are deployed in this study, they are decision tree, naïve bayes, k-nearest neighbors, support vector machine, random forest, logistic regression, neural network, and gradient boosting.This research uses several stages of research such as load dataset, preprocessing data, data modeling, evaluation of various data modelling, and input new data.  The best performance was produced by the random forest model with an accuracy value of 0.84, a precision value of 0.84, a recall value of 0.85, an f1-score value of 0.79 and an AUC value of 0.72. The prediction generated by the proposed machine learning model is high risk or low risk of CVD.


Keywords


Machine Learning; CVD; Framingham Heart Study

  
  

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doi  https://doi.org/10.33096/ilkom.v16i1.1952.68-75
  

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References


A. Garg, B. Sharma, and R. Khan, “Heart disease prediction using machine learning techniques,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1022, no. 1, p. 12046, Jan. 2021, doi: 10.1088/1757-899X/1022/1/012046.

N. R. Panda, K. L. Mahanta, J. K. Pati, R. Bhuyan, and S. S. Satapathy, “The Effectiveness of Machine Learning Systems’ Accuracy in Predicting Heart Stroke Using Socio-Demographic and Risk Factors-A Comparative Analysis of Various Models,” Natl. J. Community Med., vol. 14, no. 6, pp. 371–378, 2023, doi: 10.55489/njcm.140620233026.

R. Agrawal, M. K. Gourisaria, and S. K. Panda, “Diagnosis of Heart Stroke Using Feature Extraction and Sequential Learning Based Models,” in 2022 10th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing (ICETET-SIP-22), 2022, pp. 1–7, doi: 10.1109/ICETET-SIP-2254415.2022.9791712.

World Heart Report 2023, “World Heart Report 2023 Confronting the World’s Number One Killer,” 2023.

F. Ewbank, J. Birks, and D. Bulters, “The association between acetylsalicylic acid and subarachnoid haemorrhage: the Framingham Heart Study.,” Sci. Rep., vol. 13, no. 1, p. 6533, Apr. 2023, doi: 10.1038/s41598-023-33570-9.

K. Drożdż et al., “Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach,” Cardiovasc. Diabetol., vol. 21, no. 1, pp. 1–12, 2022, doi: 10.1186/s12933-022-01672-9.

M. Cordeiro-Costas, D. Villanueva, P. Eguía-Oller, M. Martínez-Comesaña, and S. Ramos, “Load Forecasting with Machine Learning and Deep Learning Methods,” Appl. Sci., vol. 13, no. 13, 2023, doi: 10.3390/app13137933.

P. Singh, N. Singh, K. K. Singh, and A. Singh, “Chapter 5 - Diagnosing of disease using machine learning,” in Machine Learning and the Internet of Medical Things in Healthcare, Academic Press, 2021, pp. 89–111.

C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data,” Front. Energy Res., vol. 9, 2021, doi: 10.3389/fenrg.2021.652801.

N. A. Mahoto, A. Shaikh, A. Sulaiman, M. S. Al Reshan, A. Rajab, and K. Rajab, “A machine learning based data modeling for medical diagnosis,” Biomed. Signal Process. Control, vol. 81, p. 104481, 2023, doi: https://doi.org/10.1016/j.bspc.2022.104481.

S. Gocheva-Ilieva, “Statistical Data Modeling and Machine Learning with Applications,” Mathematics, vol. 9, no. 23, 2021, doi: 10.3390/math9232997.

B. Jijo and A. Mohsin Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” J. Appl. Sci. Technol. Trends, vol. 2, pp. 20–28, Jan. 2021.

A. Nazir, A. Akhyar, M. Ramadhani, and Herlina, “Naive Bayes Method for Classification of Student Interest Based on Website Accessed,” J. Phys. Conf. Ser., vol. 1655, no. 1, p. 12104, Oct. 2020, doi: 10.1088/1742-6596/1655/1/012104.

R. Achmad and A. S. Girsang, “Implementation of naive bayes classifier algorithm in classification of civil servants,” in Journal of Physics: Conference Series, 2020, vol. 1485, no. 1, doi: 10.1088/1742-6596/1485/1/012018.

P. Cunningham and S. J. Delany, “k-Nearest Neighbour Classifiers - A Tutorial,” {ACM} Comput. Surv., vol. 54, no. 6, pp. 1–25, Jul. 2021, doi: 10.1145/3459665.

S. Uddin, I. Haque, H. Lu, M. A. Moni, and E. Gide, “Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction,” Sci. Rep., vol. 12, no. 1, p. 6256, 2022, doi: 10.1038/s41598-022-10358-x.

M. Schonlau and R. Y. Zou, “The random forest algorithm for statistical learning,” Stata J., vol. 20, no. 1, pp. 3–29, 2020, doi: 10.1177/1536867X20909688.

T. Ciu and R. S. Oetama, “Logistic Regression Prediction Model for Cardiovascular Disease,” IJNMT (International J. New Media Technol., vol. 7, no. 1, pp. 33–38, 2020, doi: 10.31937/ijnmt.v7i1.1340.

R. Y. Choi, A. S. Coyner, J. Kalpathy-Cramer, M. F. Chiang, and J. Peter Campbell, “Introduction to machine learning, neural networks, and deep learning,” Transl. Vis. Sci. Technol., vol. 9, no. 2, pp. 1–12, 2020, doi: 10.1167/tvst.9.2.14.

N. Aziz, E. A. P. Akhir, I. A. Aziz, J. Jaafar, M. H. Hasan, and A. N. C. Abas, “A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems,” in 2020 International Conference on Computational Intelligence (ICCI), 2020, pp. 11–16, doi: 10.1109/ICCI51257.2020.9247843.


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