An Ensemble Model for Predicting Cardiovascular Disease utilizing Nature Inspired Optimization
DOI:
https://doi.org/10.21123/bsj.2024.9973Keywords:
BAT algorithm, Cardiovascular disease, Ensemble classifier, Gradient Boosting, Inter Quartile Range, Nature Inspired OptimizationAbstract
This paper represents an efficient model for heart disease prediction model utilizing an ensemble mechanism optimized through BAT algorithm. Worldwide mortality rates are widely acknowledged to be significantly influenced by the prevalence of cardiovascular disease, particularly in economically disadvantaged regions. The need to mitigate the potentially severe repercussions associated with this particular health concern highlights the requirement for accurate and timely outcome prediction. Proposed methodology incorporates Mutual Information for feature selection, Inter Quartile Range for outlier removal. The StandardScaler method is used to achieve feature-wise standardisation in order to mitigate any bias resulting from varying scale disparities. Gradient boosting is an ensemble technique used in model construction that is well-known for its capacity to handle missing data and produce precise predictions. The BAT algorithm is implemented, which further improves speed by utilising optimisation inspired by nature. The application of the BAT method in this particular model has yielded a notable improvement in performance, resulting in an accuracy rate of 84.94%. The precision, specificity, and sensitivity scores of the model were 76.47%, 81.88%, and 89.65%, respectively. These metrics collectively suggest a balanced performance.
Received 17/10/2023
Revised 27/02/2024
Accepted 29/02/2024
Published Online First 20/06/2024
References
World Health Organization. World Health Organization home/Health topic/cardiovascular disease. www.who.net. 2021.
Boukhatem C, Youssef HY, Nassif AB. Heart disease prediction using machine learning. In: 2022 Advances in Science and Engineering Technology International Conferences (ASET). IEEE; 2022.https://doi.org/10.1109/ASET53988.2022.9734880.
Jindal H, Agrawal S, Khera R, Jain R, Nagrath P. Heart disease prediction using machine learning algorithms. IOP Conf Ser: Mater Sci Eng. 2021 Jan 1; 1022(1): 012072.https://doi.org/10.1088/1757-899X/1022/1/012072
Bharti R, Khamparia A, Shabaz M, Dhiman G, Pande S, Singh P. Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning. Comp Intell Nurosci. 2021 Jul 1; 2021: 1-7. https://doi.org/10.1155/2021/8387680
Chang V, Bhavani VR, Xu AQ, Hossain M. An artificial intelligence model for heart disease detection using machine learning algorithms. Healthc Analytics. 2022 Nov; 2: 100016.https://doi.org/10.1016/j.health.2022.100016.
Melillo P, De Luca N, Bracale M, Pecchia L. Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability. IEEE J Biomed Health Inform. 2013 May; 17(3): 727-33. Https://doi.org/10.1109/jbhi.2013.2244902.
Ramprakash P, Sarumathi R, Mowriya R, Nithyavishnupriya S. Heart disease prediction using deep neural network. In: 2020 International Conference on Inventive Computation Technologies (ICICT). IEEE; 2020.https://doi.org/10.1109/ICICT48043.2020.9112443.
Gárate-Escamila AK, Hajjam El Hassani A, Andrès E. Classification models for heart disease prediction using feature selection and PCA. Inform Med Unlock. 2020; 19: 100330.https://doi.org/10.1016/j.imu.2020.100330.
Divya K, Akash Sirohi, Sagar Pande, Rahul Malik. An IoMT Assisted Heart Disease Diagnostic System Cognitive Internet of Medical Things for Smart Healthcare. Springer, Cham; 145-161. https://doi.org/10.1007/978-3-030-55833-8_9
Karthick K, Aruna SK, Samikannu R, Kuppusamy R, Teekaraman Y, Thelkar AR. Implementation of a Heart Disease Risk Prediction Model Using Machine Learning. Comput. Math Methods Med. 2022 May 2; 2022: 1-14. https://doi.org/10.1155/2022/6517716
Pathak Y, Shukla P, Tiwari A, Stalin S, Singh S, Shukla P. Deep Transfer Learning Based Classification Model for COVID-19 Disease. Ing Rech Biomed. 2022 Apr; 43(2): 87-92. https://doi.org/10.1016/j.irbm.2020.05.003.
Kumar NK, Sindhu GS, Prashanthi DK, Sulthana AS. Analysis and prediction of cardio vascular disease using machine learning classifiers. In: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE; 2020. https://doi.org/10.1109/ICACCS48705.2020.9074183
Nukala BT. Heart disease classification comparison among patients and normal subjects using machine learning and artificial neural network techniques. Int J Biosens Bioelectron. 2021; 7(3):77-79 . https://doi.org/10.15406/ijbsbe.2021.07.00216
Kareem AK, AL-Ani MM, Nafea AA. Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network. Baghdad Sci J. 2023; 20(3(Suppl.): 1182. https://doi.org/10.21123/bsj.2023.
Zaki SM, Jaber MM, Kashmoola MA. Diagnosing COVID-19 Infection in Chest X-Ray Images Using Neural Network. Baghdad Sci J. 2022 Dec 1; 19(6): 1356.https://doi.org/10.21123/bsj.2022.5965.
Mitra S, Majumder AB, Saha T. An observation and analysis the role of Convolutional Neural Network towards lung cancer prediction. Baghdad Sci J. 2023; 20(6(Suppl.)): 2568.
Majumder AB, Gupta S, Singh D. An ensemble heart disease prediction model bagged with Logistic Regression, naïve Bayes and K Nearest Neighbour. J Phys Conf Ser. 2022; 2286(1): 012017.https://doi.org/10.1088/1742-6596/2286/1/012017
Janosi A, Steinbrunn W, Pfisterer M, Detrano R. Heart Disease. UCI Machine Learning Repository; 1989.https://doi.org/10.24432/C52P4X.
Friedman JH. Greedy function approximation: A gradient boosting machine. Ann Stat. 2001; 29(5). https://doi.org/10.1214/aos/1013203451
Yang XS. A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization. NICSO. arXiv:1004.4170. 2010 Nov: 65-74. http://dx.doi.org/10.48550/arXiv.1004.4170
Yang X-S, Chien SF, Ting TO. Bio-inspired computation and optimization. In: Bio-Inspired Computation in Telecommunications. Elsevier. 1st Ed 2015; p. 1–21. https://doi.org/10.1016/B978-0-12-801538-4.00001-X.
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Copyright (c) 2024 Annwesha Banerjee Majumder, Somsubhra Gupta, Sourav Majumder, Dharmpal Singh
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