References
1. Pouriyeh, S., Vahid, S., Sannino, G., Pietro, G. D., Arabnia, H., Gutierrez, J.: A comprehensive
investigation and comparison of machine learning techniques in the domain of heart disease. In:
IEEE Symposium on Computers and Communication, Heraklion, Greece, pp. 1–4 (2017)
2. Alotaibi, F.S.: Implementation of machine learning model to predict heart failure disease.
Int. J. Adv. Comput. Sci. Appl. 10(6), 261–268 (2019)
3. Safdari, R., Samad-Soltani, T., GhaziSaeedi, M., Zolnoori, M.: Evaluation of classification
algorithms vs knowledge-based methods for differential diagnosis of asthma in iranian
patients. Int. J. Inform. Syst. Serv. Sect. 10(2), 22–26 (2018)
4. Tarawneh, M., Embarak, O.: Hybrid approach for heart disease prediction using data mining
techniques. ACTA Sci. Nutrit. Health 3(7), 147–151 (2019)
5. Satyanandam, N., Satyanarayana, C.: Heart disease detection using predictive optimization
techniques. Int. J. Image Graph. Signal Process. 11(9), 18–24 (2019)
6. Khourdifi, Y., Bahaj, M.: Heart disease prediction and classification using machine learning
algorithms optimized by particle swarm optimization and ant colony optimization. Int.
J. Intell. Eng. Syst. 12(1), 242–253 (2018)
7. Pathak, A.K., Arul Valan, J.: A predictive model for heart disease diagnosis using fuzzy
logic and decision tree. In: Elçi, A., Sa, P.K., Modi, Chirag N., Olague, G., Sahoo,
Manmath N., Bakshi, S. (eds.) Smart Computing Paradigms: New Progresses and
Challenges. AISC, vol. 767, pp. 131–140. Springer, Singapore (2020). https://doi.org/10.
1007/978-981-13-9680-9_10
8. Bhaskaru, O., Sree, M.: Accurate and fast diagnosis of heart disease using hybrid differential
neural network algorithm. Int. J. Eng. Adv. Technol. 8(3S), 452–457 (2019)
9. Nikam, S., Shukla, P., Shah, M.: Cardiovascular disease prediction using genetic algorithm
and neurofuzzy system. Int. J. Latest Trends Eng. Technol. 8(2), 104–110 (2017)
10. Khare, P., Burse, K.: Feature selection using genetic algorithm and classification using weka
for ovarian cancer. Int. J. Comput. Sci. Inform.Technol. 7(1), 194–196 (2016)
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons
license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative
Commons license, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter's Creative Commons license and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder.
306
F. Z. Abdeldjouad et al.
1. Pouriyeh, S., Vahid, S., Sannino, G., Pietro, G. D., Arabnia, H., Gutierrez, J.: A comprehensive
investigation and comparison of machine learning techniques in the domain of heart disease. In:
IEEE Symposium on Computers and Communication, Heraklion, Greece, pp. 1–4 (2017)
2. Alotaibi, F.S.: Implementation of machine learning model to predict heart failure disease.
Int. J. Adv. Comput. Sci. Appl. 10(6), 261–268 (2019)
3. Safdari, R., Samad-Soltani, T., GhaziSaeedi, M., Zolnoori, M.: Evaluation of classification
algorithms vs knowledge-based methods for differential diagnosis of asthma in iranian
patients. Int. J. Inform. Syst. Serv. Sect. 10(2), 22–26 (2018)
4. Tarawneh, M., Embarak, O.: Hybrid approach for heart disease prediction using data mining
techniques. ACTA Sci. Nutrit. Health 3(7), 147–151 (2019)
5. Satyanandam, N., Satyanarayana, C.: Heart disease detection using predictive optimization
techniques. Int. J. Image Graph. Signal Process. 11(9), 18–24 (2019)
6. Khourdifi, Y., Bahaj, M.: Heart disease prediction and classification using machine learning
algorithms optimized by particle swarm optimization and ant colony optimization. Int.
J. Intell. Eng. Syst. 12(1), 242–253 (2018)
7. Pathak, A.K., Arul Valan, J.: A predictive model for heart disease diagnosis using fuzzy
logic and decision tree. In: Elçi, A., Sa, P.K., Modi, Chirag N., Olague, G., Sahoo,
Manmath N., Bakshi, S. (eds.) Smart Computing Paradigms: New Progresses and
Challenges. AISC, vol. 767, pp. 131–140. Springer, Singapore (2020). https://doi.org/10.
1007/978-981-13-9680-9_10
8. Bhaskaru, O., Sree, M.: Accurate and fast diagnosis of heart disease using hybrid differential
neural network algorithm. Int. J. Eng. Adv. Technol. 8(3S), 452–457 (2019)
9. Nikam, S., Shukla, P., Shah, M.: Cardiovascular disease prediction using genetic algorithm
and neurofuzzy system. Int. J. Latest Trends Eng. Technol. 8(2), 104–110 (2017)
10. Khare, P., Burse, K.: Feature selection using genetic algorithm and classification using weka
for ovarian cancer. Int. J. Comput. Sci. Inform.Technol. 7(1), 194–196 (2016)
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons
license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative
Commons license, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter's Creative Commons license and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder.
306
F. Z. Abdeldjouad et al.