The Effectiveness of Machine Learning Systems' Accuracy in Predicting Heart Stroke Using Socio-Demographic and Risk Factors - A Comparative Analysis of Various Models

Nihar Ranjan Panda; Kamal Lochan Mahanta; Jitendra Kumar Pati; Ruchi Bhuyan; Soumya subhashree  satapathy

doi:10.55489/njcm.140620233026

Authors

Nihar Ranjan Panda CV Raman Global University, Bhubaneswar, Odisha, India
Kamal Lochan Mahanta CV Raman Global University, Bhubaneswar, Odisha, India
Jitendra Kumar Pati Kiit International School, KIIT University, Bhubaneswar, Odisha, India
Ruchi Bhuyan IMS and SUM Hospital, SOA deemed to be University, Bhubaneswar, Odisha, India
Soumya subhashree satapathy Center for Biotechnology, School of Pharmaceutical Science, SOA deemed to be University, Bhubaneswar, Odisha, India

DOI:

https://doi.org/10.55489/njcm.140620233026

Keywords:

Machine learning, cardiovascular disease, neural network, Prediction

Abstract

Background: Cardiologists can more appropriately classify patients' cardiovascular diseases by executing accurate diagnoses and prognoses, enabling them to administer the most appropriate care. Due to machine learning's ability to identify patterns in data, its applications in the medical sector have grown. Diagnosticians can avoid making mistakes by classifying the incidence of cardiovascular illness using machine learning. To lower the fatality rate brought on by cardiovascular disorders, our research developed a model that can correctly forecast these conditions.

Methods: This study emphasized a model that can correctly forecast cardiovascular illnesses to lower the death rate brought on by these conditions. We deployed four well-known classification machine learning algorithms like K nearest Neighbour, Logistic Regression, Artificial Neural network, and Decision tree.

Results: The proposed models were evaluated by their performance matrices. However logistic regression performed high accuracy concerning AUC (0.955) 95% CI (0.872-0.965) followed by the artificial neural network. AUC (0.864) 95% CI (0.826-0.912).

Conclusion: Individuals' risk of having a cardiac event may be predicted using machine learning, and those who are most at risk can be identified. Predictive models may be developed via machine learning to pinpoint those who have a high chance of suffering a heart attack.

References

Mukherjee D, Patil CG. Epidemiology and the global burden of stroke. World neurosurgery. 2011 Dec 1;76(6):S85-90. Doi: https://doi.org/10.1016/j.wneu.2011.07.023 PMid:22182277

Benziger CP, Roth GA, Moran AE. The global burden of disease study and the preventable burden of NCD. Global heart. 2016 Dec 1;11(4):393-7. Doi: https://doi.org/10.1016/j.gheart.2016.10.024 PMid:27938824

Tran DM, Lekhak N, Gutierrez K, Moonie S. Risk factors associated with cardiovascular disease among adult Nevadans. PloS one. 2021 Feb 17;16(2):e0247105. Doi: https://doi.org/10.1371/journal.pone.0247105 PMid:33596242 PMCid:PMC7888645

Jahmunah V, Oh SL, Wei JK, Ciaccio EJ, Chua K, San TR, Acharya UR. Computer-aided diagnosis of congestive heart failure using ECG signals-a review. Physica Medica. 2019 Jun 1;62:95-104. Doi: https://doi.org/10.1016/j.ejmp.2019.05.004 PMid:31153403

Bhatt CM, Patel P, Ghetia T, Mazzeo PL. Effective Heart Disease Prediction Using Machine Learning Techniques. Algorithms. 2023 Feb 6;16(2):88. Doi: https://doi.org/10.3390/a16020088

Hankey GJ. Potential new risk factors for ischemic stroke: what is their potential?. Stroke. 2006 Aug 1;37(8):2181-8. Doi: https://doi.org/10.1161/01.STR.0000229883.72010.e4 PMid:16809576

WHO MONICA Project Principal Investigators. The World Health Organization MONICA Project (monitoring trends and determi-nants in cardiovascular disease): a major international collaboration. Journal of clinical epidemiology. 1988 Jan 1;41(2):105-14. Doi: https://doi.org/10.1016/0895-4356(88)90084-4 PMid:3335877

Vymazal J, Rulseh AM, Keller J, Janouskova L. Comparison of CT and MR imaging in ischemic stroke. Insights into imaging. 2012 Dec;3(6):619-27. Doi: https://doi.org/10.1007/s13244-012-0185-9 PMid:23055115 PMCid:PMC3505566

Saxena K, Sharma R. Efficient heart disease prediction system. Procedia Computer Science. 2016 Jan 1;85:962-9. Doi: https://doi.org/10.1016/j.procs.2016.05.288

Saw M, Saxena T, Kaithwas S, Yadav R, Lal N. Estimation of prediction for getting heart disease using logistic regression model of ma-chine learning. In2020 International Conference on Computer Communication and In formatics (ICCCI) 2020 Jan 22 (pp. 1-6). IEEE. Doi: https://doi.org/10.1109/ICCCI48352.2020.9104210 PMid:32983372

Alaa AM, Bolton T, Di Angelantonio E, Rudd JH, Van der Schaar M. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants. PloS one. 2019 May 15;14(5):e0213653. Doi: https://doi.org/10.1371/journal.pone.0213653 PMid:31091238 PMCid:PMC6519796

Abd Mizwar AR, Sunyoto A, Arief MR. Stroke Prediction using Machine Learning Method with Extreme Gradient Boosting Algo-rithm. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer. 2022 Jul 23;21(3):595-606. Doi: https://doi.org/10.30812/matrik.v21i3.1666

Uddin S, Haque I, Lu H, Moni MA, Gide E. Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its differ-ent variants for disease prediction. Scientific Reports. 2022 Apr 15;12(1):1-1. Doi: https://doi.org/10.1038/s41598-022-10358-x PMid:35428863 PMCid:PMC9012855

Deng Z, Zhu X, Cheng D, Zong M, Zhang S. Efficient kNN classification algorithm for big data. Neurocomputing. 2016 Jun 26;195:143-8. Doi: https://doi.org/10.1016/j.neucom.2015.08.112

Zhao D, Hu X, Xiong S, Tian J, Xiang J, Zhou J, Li H. K-means clustering and kNN classification based on negative databases. Applied soft computing. 2021 Oct 1;110:107732. Doi: https://doi.org/10.1016/j.asoc.2021.107732

Panda NR. A Review on Logistic Regression in Medical Research. National Journal of Community Medicine. 2022 Apr 30;13(04):265-70. Doi: https://doi.org/10.55489/njcm.134202222

Cheng W, Hüllermeier E. Combining instance-based learning and logistic regression for multilabel classification. Machine Learning. 2009 Sep;76:211-25. Doi: https://doi.org/10.1007/s10994-009-5127-5

Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T. Recent advances in convolutional neural net-works. Pattern recognition. 2018 May 1;77:354-77. Doi: https://doi.org/10.1016/j.patcog.2017.10.013

Aghdam HH, Heravi EJ. Guide to convolutional neural networks. New York, NY: Springer. 2017;10(978-973):51.

Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M. Graph neural networks: A review of methods and applications. AI open. 2020 Jan 1;1:57-81. Doi: https://doi.org/10.1016/j.aiopen.2021.01.001

Qadir Z, Ever E, Batunlu C. Use of neural network based prediction algorithms for powering up smart portable accessories. Neural Processing Letters. 2021 Feb;53:721-56. Doi: https://doi.org/10.1007/s11063-020-10397-3

Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H. Assessing the accuracy of prediction algorithms for classification: an over-view. Bioinformatics. 2000 May 1;16(5):412-24. Doi: https://doi.org/10.1093/bioinformatics/16.5.412 PMid:10871264

Pathak S, Mishra I, Swetapadma A. An assessment of decision tree based classification and regression algorithms. In2018 3rd Inter-national Conference on Inventive Computation Technologies (ICICT) 2018 Nov 15 (pp. 92-95). IEEE. Doi: https://doi.org/10.1109/ICICT43934.2018.9034296

Song YY, Ying LU. Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry. 2015 Apr 4;27(2):130.

Priyam A, Abhijeeta GR, Rathee A, Srivastava S. Comparative analysis of decision tree classification algorithms. International Journal of current engineering and technology. 2013 Jun 2;3(2):334-7.

Charbuty B, Abdulazeez A. Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Tech-nology Trends. 2021 Mar 24;2(01):20-8. Doi: https://doi.org/10.38094/jastt20165

Vanisree K, Singaraju J. Decision support system for congenital heart disease diagnosis based on signs and symptoms using neural networks. International Journal of computer applications. 2011 Apr 6;19(6):6-12. Doi: https://doi.org/10.5120/2368-3115

Absar N, Das EK, Shoma SN, Khandaker MU, Miraz MH, Faruque MR, Tamam N, Sulieman A, Pathan RK. The efficacy of machine-learning-supported smart system for heart disease prediction. InHealthcare 2022 Jun 18 (Vol. 10, No. 6, p. 1137). MDPI. Doi: https://doi.org/10.3390/healthcare10061137 PMid:35742188 PMCid:PMC9222326

Drożdż K, Nabrdalik K, Kwiendacz H, Hendel M, Olejarz A, Tomasik A, Bartman W, Nalepa J, Gumprecht J, Lip GY. Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach. Cardiovascular Dia-betology. 2022 Dec;21(1):1-2. Doi: https://doi.org/10.1186/s12933-022-01672-9 PMid:36371249 PMCid:PMC9655870

Rajathi S, Radhamani G. Prediction and analysis of Rheumatic heart disease using kNN classification with ACO. In2016 International Conference on Data Mining and Advanced Computing (SAPIENCE) 2016 Mar 16 (pp. 68-73). IEEE. Doi: https://doi.org/10.1109/SAPIENCE.2016.7684132

Shah D, Patel S, Bharti SK. Heart disease prediction using machine learning techniques. SN Computer Science. 2020 Nov;1:1-6. Doi: https://doi.org/10.1007/s42979-020-00365-y

Alotaibi FS. Implementation of machine learning model to predict heart failure disease. International Journal of Advanced Computer Science and Applications. 2019;10(6). Doi: https://doi.org/10.14569/IJACSA.2019.0100637