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Decision tree To classify patients Yang et al. [35] SVM models Heart attack prediction Son et al. [36] Logistic regression models Distinguish between CHF and shortness of breath problems Masetic et al. [37] Random forest, C4.5, SVM, ANN, k-NN Detect CHF Wu et al. [38] SVM, boosting, logistic regression Detect HF Aljaaf et al. [39] C4.5 Risk assessment for HF Zheng et al. [40] LS- SVM HF diagnosis Pattekari et al. [41] Naive Bayes HF prediction Takcı [42] 12 classification algorithms Heart attack detection

      1.7 Conclusion and Future Directions

The heart is a vital organ for human beings. When it cannot healthily fulfill its duty it affects the quality of life and may even cause death. The inability of the heart to fully perform its functions, especially due to a heart attack, is called “heart failure.” There are several types and many causes of HF. High blood pressure, abnormal heart valves, and damage to the heart muscle are just a few of these causes.

      Because of the importance of HF, its diagnosis is also very important. With timely diagnosis, heart patients are more likely to improve their quality of life and survive longer. Invasive and non-invasive procedures are classically used to diagnose the disease. Artificial intelligence and machine learning techniques have also been used for exploratory purposes due to the excessive causes of disease, the increasing number of patients, and patient data. Expert systems, image processing, machine learning, deep learning, and others, which are important fields of study of artificial intelligence, have been used extensively in this field recently. Artificial intelligence based methods both obtain data that could not be obtained before, and analyze previously obtained data more intelligently. Modeling of experiences with the help of machine learning and the use of models in diagnosing new patients have led to a striking development.

      The most valuable contribution of artificial intelligence, especially machine learning techniques, to this field is their role in clinical decision support systems. Decision support systems, which were previously developed based on knowledge, now work based on data, thanks to machine learning. Machine learning models trained with disease data and previously given class information have become able to diagnose automatically. Clinical decision support systems, which have reached a certain stage with machine learning systems, have also reached a higher level with deep learning algorithms. Increasing data, complicated algorithm structures, and data relationships that could not be seen before are developing this field.

      With a data-oriented and integrated clinical support system, a holistic medicine approach will now be applicable and the relationship between heart diseases and other diseases and even behavioral styles will be revealed. Diagnoses will become faster and more accurate through a more powerful data highway to be achieved thanks to 5G, the wider data holding capacity of cloud systems, more processing power from distributed processing architecture, and more complex and powerful algorithms with deep learning.

      References

      1 1 Mayo Clinic. Heart Failure. https://www.mayoclinic.org/diseases-conditions/heart-failure/symptoms-causes/syc-20373142, Access date: May 1, 2021.

      2 2 MedicalPark, Kalp Yetmezliği Nedir? Belirtileri nelerdir? https://www.medicalpark.com.tr/kalp-yetmezligi/hg-2209. Access date: April 23, 2021.

      3 3 Cousins, S., Blencowe, N.S., and Blazeby, J.M.(2019). What is an invasive procedure? A definition to inform study design, evidence synthesis and research tracking. BMJ Open 9 (7): e028576. https://doi.org/10.1136/bmjopen-2018-028576.

      4 4 Invasive Tests and Procedures. Heart Attack and Stroke Symptoms. https://www.heart.org/en/health-topics/heart-attack/diagnosing-a-heart-attack/invasive-tests-and-procedures. Access date: April 23, 2020.

      5 5 Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., et al. (2019). Cardiologistlevel arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine 25 (1): 65.

      6 6 Shamsham, Fadi, M.D.and Mitchell, Judith, M.D. (2000). Essentials of the diagnosis of HF. AFP Journal. Mar. 1, 2000. https://www.aafp.org/afp/2000/0301/p1319.html.

      7 7 Ponikowski, P., Voors, A.A., Anker, S.D., et al. (2016). ESC guidelines for the diagnosis and treatment of acute and chronic HF: The Task Force for the diagnosis and treatment of acute and chronic HF of the European Society of Cardiology (ESC). Developed with the special contribution of the HF Association (HFA) of the ESC. European Journal of Heart Failure 18 (8) (2016): 891–975.

      8 8 Attia, Z.I., Kapa, S., Lopez-Jimenez, F., et al. (2019). Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nature Medicine 25 (1) (2019): 70.

      9 9 Non-Invasive Tests and Procedures. Heart Attack and Stroke Symptoms. https://www.heart.org/en/health-topics/heart-attack/diagnosing-a-heart-attack/noninvasive-tests-and-procedures. Access date: May 20, 2021.

      10 10 Haider, D., Yang, X., and Abbasi, Q.H. (2019). Post-surgical fall detection by exploiting the 5 G C-Band technology for eHealth paradigm. Applied Soft Computing 81: 105537.

      11 11 Sahoo, S.P.and Ari, S. (2019). On an algorithm for human action recognition. Expert Systems With Applications 115: 524–534.

      12 12 Li, C., Hu, X., and Zhang, L. (2017). The IoT-based heart disease monitoring system for pervasive healthcare service. Procedia Computer Science 112: 2328–2334. (2017).

      13 13 Yahaya, S.W., Lotfi, A., and Mahmud, M.A. (2019). Consensus novelty detection ensemble approach for anomaly detection in activities of daily living. Applied Soft Computing

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