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Impact of computing platforms on classifier performance in heart disease prediction

Authors

  • Beenish Ayesha Akram

    Department of Computer Engineering, University of Engineering and Technology, Lahore, Pakistan
    Author

  • Muhammad Irfan

    Department of Computer Engineering, University of Engineering and Technology, Lahore, Pakistan
    Author

  • Amna Zafar

    Department of Computer Science, University of Engineering and Technology, Lahore, Pakistan
    Author

  • Sidra Khan

    Department of Computer Engineering, University of Engineering and Technology, Lahore, Pakistan
    Author

  • Rubina Shaheen

    Department of Computer Engineering, University of Engineering and Technology, Lahore, Pakistan
    Author

DOI:

https://doi.org/10.22581/muet1982.3268

Keywords:

Heart disease prediction, Support Vector Machine, Classification, Machine learning, Classification metrics, Platform Comparison

Abstract

Prediction and classification, a supervised learning technique in machine learning, addresses various challenges related to finding useful patterns present in data. This work explores how different computing platforms influence the accuracy of classification results when employing the same models. Heart disease, a widespread global health issue affecting both men and women, results from a complex interplay of lifestyle factors and genetics. Through visual representations, we examined the diverse factors influencing heart stroke occurrences. We employed multiple classification methods such as Logistic Regression, K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, and Decision Tree (DT), assessing their accuracy using WEKA and Google Colab (using Scikit-Learn library). Our evaluation revealed that SVM achieves 77% accuracy when implemented using Scikit-Learn, demonstrating superiority over other methods. However, when using WEKA, both logistic regression and SVM demonstrated nearly 91% accuracy using the exact same hyperparameters. This research demonstrated the significance of platform selection in influencing classifier performance, offering valuable insights on how results reported in research can be impacted by the selection of the software and tools, using heart disease prediction as a use case scenario.

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References

“WHO / cardiovascular diseases (CVDs)”, Accessed: Mar. 09, 2024. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)

WHO, “Noncommunicable diseases country profiles 2018”, Accessed: May 09, 2024. [Online]. Available: https://www.who.int/publications/i/item/9789241514620

K. Mohtadi et al., “Current Risk Factors of Ischemic Cardiovascular Diseases Estimated in a Representative Population of Casablanca”, Endocrinol Metab Syndr, vol. 07, no. 01, 2018, doi: 10.4172/2161-1017.1000284. DOI: https://doi.org/10.4172/2161-1017.1000284

B. J. W. Chow et al., “Interpreting Wide-Complex Tachycardia With the Use of Artificial Intelligence,” Canadian Journal of Cardiology, vol. 40, no. 10, pp. 1965–1973, Oct. 2024, doi: 10.1016/j.cjca.2024.03.027.

B. Martins, D. Ferreira, C. Neto, A. Abelha, and J. Machado, “Data Mining for Cardiovascular Disease Prediction”, J Med Syst, vol. 45, no. 1, p. 6, Jan. 2021, doi: 10.1007/s10916-020-01682-8.

V. K and J. Singaraju, “Decision Support System for Congenital Heart Disease Diagnosis based on Signs and Symptoms using Neural Networks”, IJCA, vol. 19, no. 6, pp. 6–12, Apr. 2011, doi: 10.5120/2368-3115. DOI: https://doi.org/10.5120/2368-3115

S. Wang and R. M. Summers, “Machine learning and radiology”, Medical Image Analysis, vol. 16, no. 5, pp. 933–951, Jul. 2012, doi: 10.1016/j.media.2012.02.005. DOI: https://doi.org/10.1016/j.media.2012.02.005

J. Soni, U. Ansari, D. Sharma, and S. Soni, “Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction”, IJCA, vol. 17, no. 8, pp. 43–48, Mar. 2011, doi: 10.5120/2237-2860. DOI: https://doi.org/10.5120/2237-2860

A. Benba, A. Jilbab, and A. Hammouch, “Discriminating Between Patients With Parkinson’s and Neurological Diseases Using Cepstral Analysis”, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 24, no. 10, pp. 1100–1108, Oct. 2016, doi: 10.1109/TNSRE.2016.2533582. DOI: https://doi.org/10.1109/TNSRE.2016.2533582

I. Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas, and I. Chouvarda, “Machine Learning and Data Mining Methods in Diabetes Research”, Computational and Structural Biotechnology Journal, vol. 15, pp. 104–116, 2017, doi: 10.1016/j.csbj.2016.12.005. DOI: https://doi.org/10.1016/j.csbj.2016.12.005

P. I. Dorado-Díaz, J. Sampedro-Gómez, V. Vicente-Palacios, and P. L. Sánchez, “Applications of Artificial Intelligence in Cardiology. The Future is Already Here”, Revista Española de Cardiología (English Edition), vol. 72, no. 12, pp. 1065–1075, Dec. 2019, doi: 10.1016/j.rec.2019.05.014.

Q. K. Al-Shayea, “Artificial Neural Networks in Medical Diagnosis”, vol. 8, no. 2, 2011.

M. A. Jabbar, B. L. Deekshatulu, and P. Chandra, “Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm”, Procedia Technology, vol. 10, pp. 85–94, 2013, doi: 10.1016/j.protcy.2013.12.340. DOI: https://doi.org/10.1016/j.protcy.2013.12.340

C. S.Dangare and S. S. Apte, “Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques”, IJCA, vol. 47, no. 10, pp. 44–48, Jun. 2012, doi: 10.5120/7228-0076. DOI: https://doi.org/10.5120/7228-0076

Y. Xing, J. Wang, Z. Zhao, and andYonghong Gao, “Combination Data Mining Methods with New Medical Data to Predicting Outcome of Coronary Heart Disease”, in 2007 International Conference on Convergence Information Technology (ICCIT 2007), Gyeongju-si, Gyeongbuk, Korea: IEEE, Nov. 2007, pp. 868–872. doi: 10.1109/ICCIT.2007.204. DOI: https://doi.org/10.1109/ICCIT.2007.204

X. Liu et al., “A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method”, Computational and Mathematical Methods in Medicine, vol. 2017, pp. 1–11, 2017, doi: 10.1155/2017/8272091. DOI: https://doi.org/10.1155/2017/8272091

F. Tasnim and S. U. Habiba, “A Comparative Study on Heart Disease Prediction Using Data Mining Techniques and Feature Selection”, in 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), DHAKA, Bangladesh: IEEE, Jan. 2021, pp. 338–341. doi: 10.1109/ICREST51555.2021.9331158.

J. Nahar, T. Imam, K. S. Tickle, and Y.-P. P. Chen, “Computational intelligence for heart disease diagnosis: A medical knowledge driven approach”, Expert Systems with Applications, vol. 40, no. 1, pp. 96–104, Jan. 2013, doi: 10.1016/j.eswa.2012.07.032. DOI: https://doi.org/10.1016/j.eswa.2012.07.032

Y. Christian, “Comparison of Machine Learning Algorithms Using WEKA and Sci-Kit Learn in Classifying Online Shopper Intention”, JITE, vol. 3, no. 1, pp. 58–66, Jul. 2019, doi: 10.31289/jite.v3i1.2599.

“UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA.”, [Online]. Available: https://scirp.org/reference/referencespapers?referenceid=2607575

A. K. Dwivedi, “Performance evaluation of different machine learning techniques for prediction of heart disease”, Neural Comput & Applic, vol. 29, no. 10, pp. 685–693, May 2018, doi: 10.1007/s00521-016-2604-1. DOI: https://doi.org/10.1007/s00521-016-2604-1

K. Uyar and A. İlhan, “Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks”, Procedia Computer Science, vol. 120, pp. 588–593, 2017, doi: 10.1016/j.procs.2017.11.283. DOI: https://doi.org/10.1016/j.procs.2017.11.283

R. Shishodia and P. Anand, “Analysis Of Heart Disease Using Orange And R”, 2019.

J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan, and A. Saboor, “Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare”, IEEE Access, vol. 8, pp. 107562–107582, 2020, doi: 10.1109/ACCESS.2020.3001149.

A. Desai and D. S. Rai, “Analysis of Machine Learning Algorithms using Weka”, International Journal of Computer Applications.

Susanto, D. Stiawan, M. A. S. Arifin, Mohd. Y. Idris, and R. Budiarto, “IoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning”, in 2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI), Yogyakarta, Indonesia: IEEE, Oct. 2020, pp. 15–20. doi: 10.23919/EECSI50503.2020.9251304.

B. O. Awojoyogbe and M. O. Dada, Digital Molecular Magnetic Resonance Imaging. in Series in BioEngineering. Singapore: Springer Nature Singapore, 2024. doi: 10.1007/978-981-97-6370-2.

https://www.kaggle.com/code/ricksan4ez/heartdisease-visualisation-classification/input

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Published

2025-04-09

Issue

Vol. 44 No. 2 (2025): April Issue

Section

Articles

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Copyright (c) 2025 Mehran University Research Journal of Engineering and Technology

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Impact of computing platforms on classifier performance in heart disease prediction. (2025). Mehran University Research Journal of Engineering and Technology, 44(2), 155-163. https://doi.org/10.22581/muet1982.3268

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