Abstract
Objectives
This research work exclusively aims to develop a novel heart disease prediction framework including three major phases, namely proposed feature extraction, dimensionality reduction, and proposed ensemble-based classification.
Methods
As the novelty, the training of NN is carried out by a new enhanced optimization algorithm referred to as Sea Lion with Canberra Distance (S-CDF) via tuning the optimal weights. The improved S-CDF algorithm is the extended version of the existing “Sea Lion Optimization (SLnO)”. Initially, the statistical and higher-order statistical features are extracted including central tendency, degree of dispersion, and qualitative variation, respectively. However, in this scenario, the “curse of dimensionality” seems to be the greatest issue, such that there is a necessity of dimensionality reduction in the extracted features. Hence, the principal component analysis (PCA)-based feature reduction approach is deployed here. Finally, the dimensional concentrated features are fed as the input to the proposed ensemble technique with “Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN)” with optimized Neural Network (NN) as the final classifier.
Results
An elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques.
Conclusions
From the experiment outcomes, it is proved that the accuracy of the proposed work with the proposed feature set is 5, 42.85, and 10% superior to the performance with other feature sets like central tendency + dispersion feature, central tendency qualitative variation, and dispersion qualitative variation, respectively.
Results
Finally, the comparative evaluation shows that the presented work is appropriate for heart disease prediction as it has high accuracy than the traditional works.
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Research funding: None declared.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication. The authors declare that they have no conflict of interest.
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Ethical approval: The conducted research is not related to either human or animal use.
References
1. Bojja, GR, Ofori, M, Liu, J, Ambati, LS. Early public outlook on the coronavirus disease (COVID-19): a social media study; 2020.Search in Google Scholar
2. Mienye, ID, Sun, Y, Wang, Z. Improved sparse autoencoder based artificial neural network approach for prediction of heart disease. Inf Med Unlocked 2020;18:100307. https://doi.org/10.1016/j.imu.2020.100307.Search in Google Scholar
3. Al-Makhadmeh, Z, Tolba, A. Utilizing IoT wearable medical device for heart disease prediction using higher order Boltzmann model: a classification approach. Measurement 2019;147:106815. https://doi.org/10.1016/j.measurement.2019.07.043.Search in Google Scholar
4. Rodríguez, J, Prieto, S, Lópe, LJR. A novel heart rate attractor for the prediction of cardiovascular disease. Inf Med Unlocked 2019;15:100174. https://doi.org/10.1016/j.imu.2019.100174.Search in Google Scholar
5. Baggen, VJM, Venema, E, Živná, R, Bosch, AE, Roos-Hesselink, JW. Development and validation of a risk prediction model in patients with adult congenital heart disease. Int J Cardiol 2019;276:87–92. https://doi.org/10.1016/j.ijcard.2018.08.059.Search in Google Scholar PubMed
6. Ong, KL, Chung, RWS, Hui, N, Festin, K, Kristenson, M. Usefulness of certain protein biomarkers for prediction of coronary heart disease. Am J Cardiol 2020;125:542–8. https://doi.org/10.1016/j.amjcard.2019.11.016.Search in Google Scholar PubMed
7. Patel, J, Rifai, MA, Scheuner, MT, Shea, S, Evoy, JWM. Basic vs. more complex definitions of family history in the prediction of coronary heart disease: the multi-ethnic study of atherosclerosis. Mayo Clin Proc 2018;93:1213–23. https://doi.org/10.1016/j.mayocp.2018.01.014.Search in Google Scholar PubMed PubMed Central
8. Rajakumar, BR, George, A. On hybridizing fuzzy min max neural network and firefly algorithm for automated heart disease diagnosis. In: 2013 fourth international conference on computing, communications and networking technologies(ICCCNT); Tiruchengode, India, IEEE 2013:1–5 pp.10.1109/ICCCNT.2013.6726611Search in Google Scholar
9. Praveena, MDA, Bharathi, B. Cognitive learning based missing value computation in cardiovascular heart disease prediction data. Procedia Comput Sci 2019;165:742–50. https://doi.org/10.1016/j.procs.2020.01.019.Search in Google Scholar
10. Beunza, J-J, Puertas, E, García-Ovejero, E, Villalba, G, Landecho, MF. Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease). J Biomed Inf 2019;97. 103257, https://doi.org/10.1016/j.jbi.2019.103257.Search in Google Scholar PubMed
11. Amin, MS, Chiam, YK, Varathan, KD. Identification of significant features and data mining techniques in predicting heart disease. Telematics Inf 2019;36:82–93. https://doi.org/10.1016/j.tele.2018.11.007.Search in Google Scholar
12. Ahmed, H, Younis, EMG, Hendawi, A, Ali, AA. Heart disease identification from patients’ social posts, machine learning solution on Spark. Future Generat Comput Syst 2020;111:714–22. https://doi.org/10.1016/j.future.2019.09.Search in Google Scholar
13. Bonacaro, A, Morgan, L. Simulated mindfulness meditation: a major breakthrough in the management of chronic pain; 2016.Search in Google Scholar
14. Harel-Sterling, L, Wang, F, Cohen, S, Liu, A, Marelli, A. Risk predictions in adult congenital heart disease patients with heart failure: a systematic review. J Am Coll Cardiol 2019;73:656. https://doi.org/10.1016/s0735-1097(19)31264-1.Search in Google Scholar
15. Hamed, MB, Farah, A, Abdeljalil, O, Garmazi, S. Metabolic factors of coronary arteries restenosis formation and unfavourable outcomes prediction of stent angioplasty in patients with chronic coronary heart disease. Arch Cardiovasc Dis Suppl 2019;11:188–9. https://doi.org/10.1016/j.acvdsp.2019.02.017.Search in Google Scholar
16. Kinoshita, T, Abe, A, Yao, S, Yano, K, Ikeda, T. Risk stratification with non-invasive techniques for prediction of cardiac mortality in patients with ischemic heart disease. J Electrocardiol 2019;53:e17–8. https://doi.org/10.1016/j.jelectrocard.2019.01.063.Search in Google Scholar
17. Bossolasco, M, Fenoglio, LM. Yet another PECS usage: a continuous PECS block for anterior shoulder surgery. J Anaesthesiol Clin Pharmacol 2018;34:569. https://doi.org/10.4103/joacp.joacp_12_18.Search in Google Scholar
18. Yang, J, Xiao, W, Lu, H, Barnawi, A. Wireless high-frequency NLOS monitoring system for heart disease combined with hospital and home. Future Generat Comput Syst 2019;110:772–80. https://doi.org/10.1016/j.future.2019.11.001.Search in Google Scholar
19. Samuel, OW, Yang, B, Geng, Y, Asogbon, MG, Li, G. A new technique for the prediction of heart failure risk driven by hierarchical neighborhood component-based learning and adaptive multi-layer networks. Future Generat Comput Syst 2019;110:781–94. https://doi.org/10.1016/j.future.2019.10.034.Search in Google Scholar
20. Kinoshita, T, Abe, A, Yao, S, Yano, K, Ikeda, T. Risk stratification with non-invasive techniques for prediction of cardiac mortality in patients with ischemic heart disease. J Electrocardiol 2018;51:1179. https://doi.org/10.1016/j.jelectrocard.2018.10.063.Search in Google Scholar
21. Wang, Z, Wang, B, Zhou, Y, Li, D, Yin, Y. Weight-based multiple empirical kernel learning with neighbor discriminant constraint for heart failure mortality prediction. J Biomed Inf 2020;101:103340. https://doi.org/10.1016/j.jbi.2019.103340.Search in Google Scholar PubMed
22. George, A, Rajakumar, BR. On hybridizing fuzzy min max neural network and firefly algorithm for automated heart disease diagnosis. In: Fourth international conference on computing, communications and networking technologies. Tiruchengode, India: IEEE; 2013:1–5 pp.10.1109/ICCCNT.2013.6726611Search in Google Scholar
23. Singh, G, Jain, VK, Singh, A. Adaptive network architecture and firefly algorithm for biogas heating model aided by photovoltaic thermal greenhouse system. Energy Environ 2018;29:1073–97. https://doi.org/10.1177/0958305x18768819.Search in Google Scholar
24. Bojja, GR, Ambati, LS. A novel framework for crop pests and disease identification using social media and AI. In: Proceedings of the fifteenth midwest association for information systems conference. Des Moines, Iowa; 2020:28–9 pp.Search in Google Scholar
25. Manassero, A, Bossolasco, M, Ugues, S, Bailo, C. An atypical case of two instances of mepivacaine toxicity. J Anaesthesiol Clin Pharmacol 2014;30:582. https://doi.org/10.4103/0970-9185.142887.Search in Google Scholar PubMed PubMed Central
26. Desogus, M. The stochastic dynamics of business evaluations using Markov models. Int J Contemp Math Sci 2020;15:53–60. https://doi.org/10.12988/ijcms.2020.91233.Search in Google Scholar
27. Thangam, T, Kazem, HA, Muthuvel, K. SFOA: Sun Flower Optimization Algorithm to Solve Optimal Power Flow J Comput Mech Power Syst Control 2019;2. Resbee Publishers.10.46253/jcmps.v2i4.a2Search in Google Scholar
28. Latha, CBC, Jeeva, SC. Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inf Med Unlocked 2019;16:100203. https://doi.org/10.1016/j.imu.2019.100203.Search in Google Scholar
29. Mathan, K, Kumar, PM, Panchatcharam, P, Manogaran, G, Varadharajan, R. A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Des Autom Embed Syst 2018;22:225–42. https://doi.org/10.1007/s10617-018-9205-4.Search in Google Scholar
30. Vijayashree, J, Sultana, HP. Heart disease classification using hybridized Ruzzo-Tompa memetic based deep trained Neocognitron neural network. Health Technol 2018;10:207–16. https://doi.org/10.1007/s12553-018-00292-2.Search in Google Scholar
31. Ali, L, Rahman, A, Khan, A, Zhou, M, Javeed, A, Khan, JA. An automated diagnostic system for heart disease prediction based on ${\chi^{2}}$ statistical model and optimally configured deep neural network. IEEE Access 2019;7:34938–45. https://doi.org/10.1109/access.2019.2904800.Search in Google Scholar
32. Javeed, A, Zhou, S, Yongjian, L, Qasim, I, Noor, A, Nour, R. An intelligent learning system based on random search algorithm and optimized random forest model for improved heart disease detection. IEEE Access 2019;7:180235–43. https://doi.org/10.1109/access.2019.2952107.Search in Google Scholar
33. Ali, L, Niamat, A, Khan, JA, Golilarz, NA, Xingzhong, X, Noor, A, et al.. An optimized stacked support vector machines based expert system for the effective prediction of heart failure. IEEE Access 2019;7:54007–14. https://doi.org/10.1109/access.2019.2909969.Search in Google Scholar
34. Maragatham, G, Devi, S. LSTM model for prediction of heart failure in big data. J Med Syst 2019;43:111. https://doi.org/10.1007/s10916-019-1243-3.Search in Google Scholar PubMed
35. Nourmohammadi-Khiarak, J, Feizi-Derakhshi, M-R, Behrouzi, K, Mazaheri, S, Zamani-Harghalani, Y, Tayebi, RM. New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection. Health Technol 2019;10:1–12. https://doi.org/10.1007/s12553-019-00396-3.Search in Google Scholar
36. Avci, E. A new intelligent diagnosis system for the heart valve diseases by using genetic-SVM classifier. Expert Syst Appl 2009;36:10618–26. https://doi.org/10.1016/j.eswa.2009.02.053.Search in Google Scholar
37. Masetic, Z, Subasi, A. Congestive heart failure detection using random forest classifier. Comput Methods Progr Biomed 2016;130:54–64. https://doi.org/10.1016/j.cmpb.2016.03.020.Search in Google Scholar PubMed
38. Jabbar, MA, Deekshatulu, BL, Chandra, P. Classification of heart disease using K- nearest neighbor and genetic algorithm. Procedia Technol 2013;10:85–94. https://doi.org/10.1016/j.protcy.2013.12.340.Search in Google Scholar
39. Central tendency. Available from: https://en.wikipedia.org/wiki/Central_tendency [Accessed 11 May 2020].Search in Google Scholar
40. Statistical dispersion. Available from: https://en.wikipedia.org/wiki/Statistical_dispersion [Accessed 11 May 2020].Search in Google Scholar
41. Qualitative variatoin. Available from: https://en.wikipedia.org/wiki/Qualitative_variation [Accessed 11 May 2020].Search in Google Scholar
42. Gárate-Escamila, AK, Hassani, AHE, Andrès, E. Classification models for heart disease prediction using feature selection and PCA. Inf Med Unlocked 2020;19:100330. https://doi.org/10.1016/j.imu.2020.100330.Search in Google Scholar
43. Masadeh, R, Mahafzah, BA, Sharieh, A. Sea Lion optimization algorithm. Int J Adv Comput Sci Appl 2019;10:388–95. https://doi.org/10.14569/ijacsa.2019.0100548.Search in Google Scholar
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