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Journal of Statistics and Management Systems

(Since 1998)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

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Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Journal of Statistics and Management Systems
Open Access Research Article

Data-driven optimization strategies for enhanced cardiovascular risk assessment

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pp. 315–325Vol. 27Issue 2March 2024DOI: 10.47974/JSMS-1257XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1257
Pages:
315–325

Abstract

Cardiovascular diseases (CVD) remain a significant global health challenge, requiring the improvement of risk assessment methods using advanced data-driven optimization strategies. This study explores the field of ML by comparing the default implementations of Random Forest (RF) and Support Vector Machine (SVM) in the context of predicting cardiovascular risk. The study examines the potential enhancements achieved by optimizing these models using Particle Swarm Optimization (PSO), a metaheuristic algorithm known for its capacity to improve complex problem-solving. The comparative analysis assesses the predictive accuracy of default RF and SVM models in identifying cardiovascular risk factors. Subsequently, a PSOtechnique is employed to optimize the parameters of both RF and SVM models, with the objective of maximizing their predictive capabilities. Remarkably, the Random Forest model, when enhanced with PSO stands out as the most precise, attaining an impressive accuracy rate of 97.4%. To strengthen the analytical process, the research includes a thorough exploratory data analysis (EDA) phase. This involves a thorough analysis of the dataset to reveal hidden patterns, correlations, and possible variables that may cause confusion. The knowledge acquired from EDA not only enhances the optimization process but also facilitates a more profound comprehension of the complex interconnections among different cardiovascular risk factors. The study highlights the crucial significance of data pre-processing in guaranteeing the dependability and resilience of the models. The application of thorough techniques for handling missing data, outliers, and normalization significantly improves the quality of input data, thereby enhancing the performance of ML models. The utilization of various methods such as comparative analysis, optimization with PSO, exploratory data analysis, and rigorous data pre-processing highlights the possibility of creating cardiovascular risk assessment tools that are both highly accurate and dependable. The results of this study add to the ongoing discussion on utilizing sophisticated data analysis techniques to enhance patient outcomes and modify healthcare interventions in the field of cardiovascular health.

Keywords

Cardiovascular risk assessmentData-driven modelingOptimization techniquesParticle swarm optimization

Subject Classifications

Primary 93A30Secondary 49K15

References

[1] J. Nourmohammadi-Khiarak, M. R. Feizi-Derakhshi, K. Behrouzi, S. Mazaheri, Y. Zamani-Harghalani, and R. M. Tayebi, “New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection,” Health Technol. (Berl)., vol. 10, no. 3, pp. 667–678 (2020), doi: 10.1007/s12553-019-00396-3.
[2] Oswald, G. J. Sathwika, and A. Bhattacharya, “Prediction of CardioVascular Disease (CVD) using Ensemble Learning Algorithms,” ACM Int. Conf. Proceeding Ser., no. Cvd, pp. 292–293 (2022), doi: 10.1145/3493700.3493747.
[3] K. M. Zubair Hasan and M. Zahid Hasan, Performance Evaluation of Ensemble-Based Machine Learning Techniques for Prediction of Chronic Kidney Disease, vol. 882. Springer Singapore (2019).
[4] M. N. Uddin and R. K. Halder, “An ensemble method based multilayer dynamic system to predict cardiovascular disease using machine learning approach,” Informatics Med. Unlocked, vol. 24, p. 100584 (2021), doi: 10.1016/j.imu.2021.100584.
[5] P. Srinivas and R. Katarya, “hyOPTXg: OPTUNA hyper-parameter optimization framework for predicting cardiovascular disease using XGBoost,” Biomed. Signal Process. Control, vol. 73, no. November 2021, p. 103456 (2022), doi: 10.1016/j.bspc.2021.103456.
[6] Y. Rimal and N. Sharma, “Hyperparameter optimization: a comparative machine learning model analysis for enhanced heart disease prediction accuracy,” Multimed. Tools Appl., no. 0123456789 (2023), doi: 10.1007/s11042-023-17273-x.
[7] K. Zarkogianni, M. Athanasiou, and A. C. Thanopoulou, “Comparison of Machine Learning Approaches Toward Assessing the Risk of Developing Cardiovascular Disease as a Long-Term Diabetes Complication,” IEEE J. Biomed. Heal. Informatics, vol. 22, no. 5, pp. 1637–1647 (2018), doi: 10.1109/JBHI.2017.2765639.
[8] D. Ramesh and Y. S. Katheria, “Ensemble method based predictive model for analyzing disease datasets: a predictive analysis approach,” Health Technol. (Berl)., vol. 9, no. 4, pp. 533–545 (2019), doi: 10.1007/s12553-019-00299-3.
[9] D. C. Yadav and S. Pal, “Prediction of heart disease using feature selection and random forest ensemble method,” Int. J. Pharm. Res., vol. 12, no. 4, pp. 56–66 (2020), doi: 10.31838/ijpr/2020.12.04.013.
[10] D. Mehanović, Z. Mašetić, and D. Kečo, “Prediction of heart diseases using majority voting ensemble method,” IFMBE Proc., vol. 73, pp. 491–498 (2020), doi: 10.1007/978-3-030-17971-7_73.
[11] V. Jothi Prakash and N. K. Karthikeyan, “Enhanced Evolutionary Feature Selection and Ensemble Method for Cardiovascular Disease Prediction,” Interdiscip. Sci. – Comput. Life Sci., vol. 13, no. 3, pp. 389–412 (2021), doi: 10.1007/s12539-021-00430-x.
[12] A. Rahim, Y. Rasheed, F. Azam, M. W. Anwar, M. A. Rahim, and A. W. Muzaffar, “An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases,” IEEE Access, vol. 9, pp. 106575–106588 (2021), doi: 10.1109/ACCESS.2021.3098688.
[13] L. Sapra, J. K. Sandhu, and N. Goyal, Intelligent Method for Detection of Coronary Artery Disease with Ensemble Approach, vol. 668. Springer Singapore (2021).
[14] F. Rustam, A. Ishaq, K. Munir, M. Almutairi, N. Aslam, and I. Ashraf, “Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction,” Diagnostics, vol. 12, no. 6, pp. 1–17 (2022), doi: 10.3390/diagnostics12061474.

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