Abstract
Heart diseases can result in certain death, if not diagnosed timely. Heart diseases have caused death irrespective of age, gender or any other demographics. Its diagnosis is, hence, very significant, and angiography is used most commonly for the stated purpose, but it is very expensive and causes side effects. Machine learning techniques are used for diagnosing heart diseases. This study utilizes Z-Alizadeh Sani heart disease dataset for experimenting four feature selection and four optimization techniques to improve the performance of a standard neural network. The experimentation has helped us in finalizing the proposed technique, and the final technique has also been applied on two other datasets in order to ensure that overfitting is avoided. The results of the final application are promising, and in the future, we intend to form a subset of those features which are selected by all the techniques. We also intend to make an ensemble of the best weight optimization techniques in order to further improve the standard neural network.
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References
Arabasadi, Z., et al.: Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Comput. Methods Programs Biomed. 141(2017), 19–26 (2017)
Bozkurt, B., Germanakis, I., Stylianou, Y.: A study of time-frequency features for CNN-based automatic heart sound classification for pathology detection. Comput. Biol. Med. 100, 132–143 (2018)
Tan, J., Hagiwara, Y., Pang, W., Lim, I., Oh, S., Adam, M., Tan, R., Chen, M., Acharya, U.: Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Comput. Biol. Med. 94, 19–26 (2018)
Dominguez-Morales, J., Jimenez-Fernandez, A., Dominguez-Morales, M., Jimenez-Moreno, G.: Deep neural networks for the recognition and classification of heart murmurs using neuromorphic auditory sensors. IEEE Trans. Biomed. Circ. Syst. 12(1), 24–34 (2018)
Jin, B., Che, C., Liu, Z., Zhang, S., Yin, X., Wei, X.: Predicting the risk of heart failure with EHR sequential data modeling. IEEE Access 6, 9256–9261 (2018)
Boon, K., et al.: Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III. Comput. Methods Programs Biomed. 153(2018), 171–184 (2018)
Mustaqeem, A., et al.: A statistical analysis based recommender model for heart disease patients. Int. J. Med. Inform. 108(2017), 134–145 (2017)
Aborokbah, M., Al-Mutairi, S., Sangaiah, A., Samuel, O.: Adaptive context aware decision computing paradigm for intensive health care delivery in smart cities—A case analysis (2018). https://www.sciencedirect.com/science/article/pii/S2210670717305000?via%3Dihub
Pławiak, P.: Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system. Expert Syst. Appl. 92, 334–349 (2018)
Tayefi, M., et al.: hs-CRP is strongly associated with coronary heart disease (CHD): a data mining approach using decision tree algorithm. Comput. Methods Programs Biomed. 141(2017), 105–109 (2017)
Mahajan, R., et al.: Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics. Int. J. Med. Inform. 108(2017), 55–63 (2017)
Miao, F., Cai, Y., Zhang, Y., Fan, X., Li, Y.: Predictive modeling of hospital mortality for patients with heart failure by using an improved random survival forest. IEEE Access 6, 7244–7253 (2018)
Mokeddem, S.: A fuzzy classification model for myocardial infarction risk assessment. Appl. Intell. 48, 1233–1250 (2017)
Rasmy, L., Wu, Y., Wang, N., Geng, X., Zheng, W., Wang, F., Wu, H., Xu, H., Zhi, D.: A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. J. Biomed. Inform. 84, 11–16 (2018)
Chen, Y.-W., Lin, C.-J.: Combining SVMs with various feature selection strategies. In: Feature Extraction: Foundations and Applications, pp. 315–324 (2006)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, New York (2013)
Pang-Ning, T., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison Wesley, Boston (2006)
Wani, F., Rizvi, S., Sharma, M., Bhat, M.: A study on cross-validation for model selection and estimation. Int. J. Agric. Sci. 14(1), 165–172 (2018)
Eberhart, R.C., Shi, Y.: Comparison between Genetic Algorithms and Particle Swarm Optimization
Samuel, O., et al.: An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction, pp. 163–172 (2016)
Bashir, S., et al.: IntelliHealth: a medical decision support application using a novel weighted multi-layer classifier ensemble framework. J. Biomed. Inform. 59(2016), 185–200 (2016)
Babič, F., Olejár, J., Vantová, Z., Paralič, J.: Predictive and descriptive analysis for heart disease diagnosis. In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems (2017). https://ieeexplore.ieee.org/document/8104531. Accessed 1 Mar 2019
Alizadehsani, R., et al.: Coronary artery disease detection using computational intelligence methods. Knowl. Based Syst. 109, 187–197 (2016). https://www.sciencedirect.com/science/article/abs/pii/S0950705116302076. Accessed 3 Mar 2019
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Khan, Y., Qamar, U., Asad, M., Zeb, B. (2020). Applying Feature Selection and Weight Optimization Techniques to Enhance Artificial Neural Network for Heart Disease Diagnosis. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_26
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DOI: https://doi.org/10.1007/978-3-030-29516-5_26
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