Hybrid Artificial Intelligence-Based Algorithm Design For Cardiovascular Disease Detection

Year 2022, Volume: 2 Issue: 2, 59 - 64, 23.09.2022

Buse Nur Karaman , Zeynep Bağdatlı , Nilay Nisa Taçyıldız , Sude Çiğnitaş , Derya Kandaz , Muhammed Kürşad Uçar

https://doi.org/10.54569/aair.1141465

Abstract

Objective: Cardiovascular Disease (CVD) is a disease that negatively affects the blood vessel system due to plaque formation as a result of accumulation on the inner wall of the vessels. In the diagnostic phase, angiography results are evaluated by physicians. New diagnostic algorithms based on artificial intelligence, including new technologies, are needed for diagnosing CVD due to the time-consuming and high cost of diagnostic methods.
Materials and Methods: The heart disease dataset available on the open-source sharing site Kaggle was used in the study. The dataset includes 14 clinical findings. In the study, after the features were selected with the Fischer feature selection algorithm, they were classified with Ensemble Decision Trees (EDT), k-Nearest Neighborhood Algorithm (kNN), and Neural Networks (NN). A hybrid artificial intelligence algorithm was also created using the three methods.
Results: According to the classification results, EDT %96.19, kNN %100, NN %86.17, and hybrid artificial intelligence determined CVD with a %99.3 success rate.
Conclusion: According to the obtained results, it is evaluated that the proposed CVD diagnosis hybrid artificial intelligence algorithms can be used in practice

Keywords

Cardiovascular Disease, Angiography, Hybrid, Artificial Intelligence, Neural Networks

References

• Onat A, Uğur M, Tuncer M, Ayhan E, Kaya Z, Küçükdurmaz Z, et al. “Age at death in the Turkish Adult Risk Factor Study: temporal trend and regional distribution at 56,700 person-years follow-up”, Türk Kardiyol Dern Arş 37(2009), 155-60.
• Üner, S, Balcılar, M ve Ergüder, T. “Türkiye hanehalkı sağlık araştırması: bulaşıcı olmayan hastalıkların risk faktörleri prevalansı”, Ankara: Dünya Sağlık Örgütü, Türkiye Ofisi, 2017.
• Liu X., Wang X., Su Qiang. “A hybrid classification system for heart disease diagnosis based on the RFRS method”, Computational and Mathematical Methods in Medicine, vol. 2017, Article ID 8272091, 11 pages, 2017. https://doi.org/10.1155/2017/8272091.
• Bulut F. “Heart attack risk detection using Bagging classifier”. 24th Signal Processing and Communication Application Conference (SIU) (pp. 2013-2016).
• Priyanka N. and Kumar P. R., “Usage of data mining techniques in predicting the heart diseases — Naïve Bayes & decision tree”, 2017 International Conference on Circuit ,Power and Computing Technologies (ICCPCT), 2017, pp. 1-7, doi: 10.1109/ICCPCT.2017.8074215.
• Taşçı M. E. ve Şamlı R., “Veri Madenciliği İle Kalp Hastalığı Teşhisi”, Avrupa Bilim ve Teknoloji Dergisi, (2020) 88-95; doi:10.31590/ejosat.araconf12.
• Eray, A., Ateş, E., & Set, T. “Yetişkin bireylerde kardiyovasküler hastalık riskinin değerlendirilmesi”. Türkiye aile hekimliği dergisi, 22 (2018), 12-19.
• Atay R., Odabaş D. E., Pehlivanoğlu M.K. (2019). “İki Seviyeli Hibrit Makine Öğrenmesi Yöntemi İle Saldırı Tespiti”, Gazi Mühendislik Bilimleri Dergisi, 5 (2019), 258-272.
• Kavitha M., Gnaneswar G., Dinesh R., Sai Y. R. and Suraj R. S., “Heart Disease Prediction using Hybrid machine Learning Model”, 6th International Conference on Inventive Computation Technologies (ICICT), 2021, pp. 1329-1333, doi: 10.1109/ICICT50816.2021.9358597.
• Nourmohammadi-Khiarak, J., Feizi-Derakhshi, MR., Behrouzi, K. et al. New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection. Health Technol. 10 (2020), 667–678. https://doi.org/10.1007/s12553-019-00396-3.
• Shah, S M, ve diğerleri. Feature extraction through parallel probabilistic principal component analysis for heart disease diagnosis. basım yeri bilinmiyor: Physica A: Statistical Mechanics and its Applications, 2017.
• Wiharto W., Kusnanto H. & Herianto H. “Hybrid system of tiered multivariate analysis and artificial neural network for coronary heart disease diagnosis”, International Journal of Electrical and Computer Engineering, 7(2), (2017) http://doi.org/10.11591/ijece.v7i2.pp1023-1031 .
• Babur, S., Turhal, U., Akbaş, A., DVM Tabanlı Kalın Bağırsak Kanseri Tanısı için Performans Geliştirme. Elektronik ve Bilgisayar Mühendisliği Sempozyumu (ELECO 2012), Bursa, 2012.
• Çalışkan, S. K., & Soğukpınar, İ., “KxKNN: K-means ve k en yakin komşu yöntemleri ile ağlarda nüfuz tespiti”, EMO Yayınları, 120-24, 2008.