Machine Learning Techniques for Heart Disease Prediction Using a Multi-Algorithm Approach

Muhammad Kunta Biddinika; Alya Masitha; Herman Herman; Vita Arfiana Nurul Fatimah

doi:10.30595/juita.v12i2.24153

Authors

Muhammad Kunta Biddinika University of Ahmad Dahlan http://orcid.org/0000-0003-4104-3755
Alya Masitha Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang
Herman Herman University of Ahmad Dahlan
Vita Arfiana Nurul Fatimah University of New South Wales

DOI:

https://doi.org/10.30595/juita.v12i2.24153

Keywords:

Machine Learning, Random Forest, heart disease, prediction

Abstract

This analysis explores the efficiency of machine learning systems for heart disease identification through a multi-algorithm approach. The main objective is to identify the best performing algorithm for accurate disease prediction, improving clinical decision making.Â Using criteria including accuracy, precision, recall, F1 score, and recall, the study assessed four algorithms: Random Forest (RF), NaÃ¯ve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT). The results show that Random Forest outperforms the others, achieving 86.23% precision, 93.76% recall, 89.84% F1 score, and 88.41% accuracy. Random Forest gets an AUC ROC result of 0.94, so Random Forest is considered a superior model in this scenario, especially because it has higher accuracy. The algorithms showed a strong balance between sensitivity and specificity. Decision Tree showed reasonable performance with a precision of 84.18% and a recall of 90.27%, while NaÃ¯ve Bayes recorded a precision of 87.68% and a recall of 87.03%. SVM showed a precision of 87.40% and a recall of 84.78%, indicating some limitations in capturing positive cases. The novelty of this study lies in the comparative analysis of several algorithms to optimize the heart disease prediction model for clinical use.Â The random forest algorithm is one of the choices, but there is still a medical standard for classifying people as either indicating or not experiencing heart failure, according to the study.

Author Biographies

Muhammad Kunta Biddinika, University of Ahmad Dahlan

Master Program in Informatics

Alya Masitha, Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang

Department of Software Engineering

Herman Herman, University of Ahmad Dahlan

Master Program in Informatics

Vita Arfiana Nurul Fatimah, University of New South Wales

Health Leadership and Management

References

[1] H. Agrawal, J. Chandiwala, S. Agrawal, and Y. Goyal, “Heart Failure Prediction using Machine Learning with Exploratory Data Analysis,” 2021 Int. Conf. Intell. Technol. CONIT 2021, 2021, doi: 10.1109/CONIT51480.2021.9498561.

[2] C. S. Wu, M. Badshah, and V. Bhagwat, “Heart disease prediction using data mining techniques,” ACM Int. Conf. Proceeding Ser., pp. 7–11, 2019, doi: 10.1145/3352411.3352413.

[3] G. Choudhary and S. Narayan Singh, “Prediction of heart disease using machine learning algorithms,” Proc. Int. Conf. Smart Technol. Comput. Electr. Electron. ICSTCEE 2020, vol. 5, no. 8, pp. 197–202, 2020, doi: 10.1109/ICSTCEE49637.2020.9276802.

[4] M. Kavitha, G. Gnaneswar, R. Dinesh, Y. R. Sai, and R. S. Suraj, “Heart Disease Prediction using Hybrid machine Learning Model,” Proc. 6th Int. Conf. Inven. Comput. Technol. ICICT 2021, pp. 1329–1333, 2021, doi: 10.1109/ICICT50816.2021.9358597.

[5] A. Masitha, M. K. Biddinika, and H. Herman, “K Value Effect on Accuracy Using the K-NN for Heart Failure Dataset,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 22, no. 3, pp. 593–604, 2023, doi: 10.30812/matrik.v22i3.2984.

[6] M. Ozcan and S. Peker, “A Classification and Regression Tree Algorithm for Heart Disease Modeling and Prediction,” Healthc. Anal., vol. 3, p. 100130, Nov. 2023, doi: 10.1016/j.health.2022.100130.

[7] D. Chicco and G. Jurman, “Machine Learning Can Predict Survival of Patients with Heart Failure from Serum Creatinine and Ejection Fraction Alone,” BMC Med. Inform. Decis. Mak., vol. 20, no. 1, pp. 1–16, 2020, doi: 10.1186/s12911-020-1023-5.

[8] B. Rahman, H. L. Hendric Spits Warnars, B. Subirosa Sabarguna, and W. Budiharto, “Heart Disease Classification Model Using K-Nearest Neighbor Algorithm,” 2021 6th Int. Conf. Informatics Comput. ICIC 2021, 2021, doi: 10.1109/ICIC54025.2021.9632918.

[9] C. B. C. Latha and S. C. Jeeva, “Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques,” Informatics Med. Unlocked, vol. 16, p. 100203, 2019, doi: 10.1016/j.imu.2019.100203.

[10] T. A. Assegie, S. J. Sushma, B. G. Bhavya, and S. Padmashree, “Correlation Analysis for Determining Effective Data in Machine Learning: Detection of Heart Failure,” SN Comput. Sci., vol. 2, no. 3, pp. 1–5, 2021, doi: 10.1007/s42979-021-00617-5.

[11] M. Peirlinck et al., “Using machine learning to characterize heart failure across the scales,” Biomech. Model. Mechanobiol., vol. 18, no. 6, pp. 1987–2001, Dec. 2019, doi: 10.1007/s10237-019-01190-w.

[12] M. Srinivasa Rao, C. Sekhar, and D. Bhattacharyya, “Comparative Analysis of Machine Learning Models on Loan Risk Analysis,” Adv. Intell. Syst. Comput., vol. 1280, pp. 81–90, 2021, doi: 10.1007/978-981-15-9516-5_7.

[13] A. Singh, H. Mahapatra, A. K. Biswal, M. Mahapatra, D. Singh, and M. Samantaray, “Heart Disease Detection Using Machine Learning Models,” Procedia Comput. Sci., vol. 235, pp. 937–947, 2024, doi: 10.1016/j.procs.2024.04.089.

[14] H. A. Taher and A. M. Abdulazeez, “Machine Learning Approaches for Heart Disease Detection: A Comprehensive Review,” Int. J. Res. Appl. Technol., vol. 3, no. 2, pp. 267–282, 2023.

[15] S. Nusinovici et al., “Logistic regression was as good as machine learning for predicting major chronic diseases,” J. Clin. Epidemiol., vol. 122, pp. 56–69, 2020, doi: 10.1016/j.jclinepi.2020.03.002.

[16] G. G. N. Geweid and M. A. Abdallah, “A new automatic identification method of heart failure using improved support vector machine based on duality optimization technique,” IEEE Access, vol. 7, pp. 149595–149611, 2019, doi: 10.1109/ACCESS.2019.2945527.

[17] J. Rashid, S. Kanwal, J. Kim, M. W. Nisar, U. Naseem, and A. Hussain, “Heart disease diagnosis using the brute force algorithm and machine learning techniques,” Comput. Mater. Contin., vol. 72, no. 2, pp. 3195–3211, 2022, doi: 10.32604/cmc.2022.026064.

[18] S. Dixit and R. Kala, “Early detection of heart diseases using a low-cost compact ECG sensor,” Multimed. Tools Appl., vol. 80, no. 21–23, pp. 32615–32637, 2021, doi: 10.1007/s11042-021-11083-9.

[19] P. Sujatha and K. Mahalakshmi, “Performance Evaluation of Supervised Machine Learning Algorithms in Prediction of Heart Disease,” 2020 IEEE Int. Conf. Innov. Technol. INOCON 2020, 2020, doi: 10.1109/INOCON50539.2020.9298354.

[20] M. Nasiruddin, S. Dutta, R. Sikder, M. R. Islam, A. Al Mukaddim, and M. A. Hider, “Predicting Heart Failure Survival with Machine Learning: Assessing My Risk,” J. Comput. Sci. Technol. Stud., vol. 6, no. 3, pp. 42–55, Aug. 2024, doi: 10.32996/jcsts.2024.6.3.5.

[21] Zulkifli, F. A. Makkiyah, D. Antoni, F. Fitriana, T. Jamaan, and A. Taufik, “Multi-Algorithm to Measure the Accuracy Level of Diabetes Status Prediction,” J. Appl. Data Sci., vol. 5, no. 2, pp. 736–746, 2024, doi: 10.47738/jads.v5i2.250.

[22] S. Sreekumari, R. Bhalla, and G. Ganesan, “Heart Disease Prediction using Machine Learning Method - A Review Article,” CEUR Workshop Proc., vol. 3635, pp. 72–82, 2023.

[23] N. Bin Noor, M. S. Anwar, and M. Dey, “Comparative Study between Decision Tree, SVM and KNN to Predict Anaemic Condition,” BECITHCON 2019 - 2019 IEEE Int. Conf. Biomed. Eng. Comput. Inf. Technol. Heal., pp. 24–28, 2019, doi: 10.1109/BECITHCON48839.2019.9063188.

[24] S. R. K. Parimi, S. Reddy, S. Jancy, L. Sujihelen, M. P. Selvan, and V. A. A. Mary, “Prediction of Multiple Diseases Using Machine Learning Techniques,” 2022 Int. Conf. Commun. Comput. Internet Things, IC3IoT 2022 - Proc., 2022, doi: 10.1109/IC3IOT53935.2022.9768024.

[25] G. S. R. Thummala and R. Baskar, “Prediction of Heart Disease using Decision Tree in Comparison with KNN to Improve Accuracy,” in Proceedings of the 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022, 2022, pp. 1–5, doi: 10.1109/ICSES55317.2022.9914044.

[26] K. Sumwiza, C. Twizere, G. Rushingabigwi, P. Bakunzibake, and P. Bamurigire, “Enhanced cardiovascular disease prediction model using random forest algorithm,” Informatics Med. Unlocked, vol. 41, 2023, doi: 10.1016/j.imu.2023.101316.

[27] A. Anderies, J. A. R. W. Tchin, P. H. Putro, Y. P. Darmawan, and A. A. S. Gunawan, “Prediction of Heart Disease UCI Dataset Using Machine Learning Algorithms,” Eng. Math. Comput. Sci. J., vol. 4, no. 3, pp. 87–93, 2022, doi: 10.21512/emacsjournal.v4i3.8683.

[28] M. Mamun, A. Farjana, M. Al Mamun, M. S. Ahammed, and M. M. Rahman, “Heart failure survival prediction using machine learning algorithm: Am i safe from heart failure?,” 2022 IEEE World AI IoT Congr. AIIoT 2022, pp. 194–200, 2022, doi: 10.1109/AIIoT54504.2022.9817303.

[29] K. M. Almustafa, “Prediction of heart disease and classifiers’ sensitivity analysis,” BMC Bioinformatics, vol. 21, no. 1, 2020, doi: 10.1186/s12859-020-03626-y.

[30] M. Mamun, A. Farjana, M. Al Mamun, M. S. Ahammed, and M. M. Rahman, “Heart failure survival prediction using machine learning algorithm: am I safe from heart failure?,” in 2022 IEEE World AI IoT Congress (AIIoT), Jun. 2022, pp. 194–200, doi: 10.1109/AIIoT54504.2022.9817303.