Main Article Content
Healthcare is very important aspects of human life. Cardiovascular disease, also known as the coronary artery disease, is one of the many deadly infections that kill people in India and around the world. Accurate predictions can prevent heart disease, but incorrect predictions can be fatal. Therefore, here this paper describes a method for predicting cardiovascular disease that makes use of Machine Learning (ML) and Deep Learning (DL). In this paper, SMOTE-ENN (Synthetic Minority Oversampling Technique Edited Nearest Neighbor) was used to equalize the distribution of training data. The K-Nearest Neighbor method (KNN), Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), XGBoost (Extreme Gradient Boosting), Artificial Neutral Network (ANN), and Convolutional Neutral Network (CNN) are among the classifiers used in this paper. From Public Health Dataset required data is collected and focused on recognizing the best approach for predicting the disease in preliminary phase. This experiment end results show that the use of Artificial Neural Networks can be of much useful in prediction with better accuracy (95.7%) than compared to any other ML approaches.
Maria Sultana Keya, Muhammad Shamsojjaman, Faruq Hossain, Farzana Akter, Fakrul Islam, Minhaz Uddin Emon, “Measuring the Heart Attack Possibility using Different Types of Machine Learning Algorithms”, 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Year: 2021.
Nafis Mostafa, Muhammad Anwarul Azim, Md Rayhan Kabir, Rasif Ajwad, “Identifying the Risk of Cardiovascular Diseases From the Analysis of Physiological Attributes”, 2020 IEEE Region 10 Symposium (TENSYMP), Year: 2020.
Saiful Islam, Nusrat Jahan, Mst. Eshita Khatun, “Cardiovascular Disease Forecast using Machine Learning Paradigms”, 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Year: 2020.
Mariusz Filipowicz, & Waleed F. Faris. (2022). Recent Advancement in the Field of Analogue Layout Synthesis. Acta Energetica, (02), 01–07. Retrieved from http://actaenergetica.org/index.php/journal/article/view/462
Mauricio Rodríguez Segura, Orietta Nicolis, Billy Peralta Márquez, Juan Carrillo Azócar, “Predicting cardiovascular disease by combining optimal feature selection methods with machine learning”, 2020 39th International Conference of the Chilean Computer Science Society (SCCC), Year: 2020.
Gudni Johannesson, & Nazzal Salem. (2022). Design Structure of Compound Semiconductor Devices and Its Applications. Acta Energetica, (02), 28–35. Retrieved from http://actaenergetica.org/index.php/journal/article/view/466
Sinkon Nayak, Mahendra Kumar Gourisaria, Manjusha Pandey, Siddharth Swarup Rautaray, “Prediction of Heart Disease by Mining Frequent Items and Classification Techniques”, 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Year: 2019.
Kunal Rajput, Girija Chetty, Rachel Davey, “Risk Factors Identification for Heart Disease in Unstructured Dataset using Deep Learning Approach”, 2019 International Conference on Data Mining Workshops (ICDMW), Year: 2019.
Yang Peili, Yin Xuezhen, Ye Jian, Yang Lingfeng, Zhao Hui, Liang Jimin, “Deep learning model management for coronary heart disease early warning research”, 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Year: 2018.
Himanshu Sharma, M A Rizvi Prediction of Heart Disease using Machine Learning Algorithms: A Survey (August 2017).
Kanna, D. R. K. ., Muda, I. ., & Ramachandran, D. S. . (2022). Handwritten Tamil Word Pre-Processing and Segmentation Based on NLP Using Deep Learning Techniques. Research Journal of Computer Systems and Engineering, 3(1), 35–42. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/39
L. Verma, S. Srivastava, and P. C. Negi, ‘‘A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data,’’ J. Med. Syst., vol. 40, no. 7, p. 178, Jul. 2016.
C.-J. Chen, Y.-T. Lo, J.-L. Huang, T.-W. Pai, M.-H. Liu, and C.-H. Wang, ‘‘Feature analysis on heart failure classes and associated medications,’’ in Proc. IEEE Int. Conf. Syst., Man, Cybern. (SMC), Oct. 2016, pp. 1382–1387.
Therasa Princy R, J. Thomas, “ Human Heart Disease Prediction System Using Data Mining Techniques”, International Conference on circuit, Power and Computing Technologies [ICCPCT],IEEE,2016.
Thota, D. S. ., Sangeetha, D. M., & Raj , R. . (2022). Breast Cancer Detection by Feature Extraction and Classification Using Deep Learning Architectures. Research Journal of Computer Systems and Engineering, 3(1), 90–94. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/48
Sana Bharti, Shailendra Narayan Singh, Amity university, Noida, India Analytical study of heart disease prediction comparing with different algorithms (May 2015).
N. C. Long, P. Meesad, and H. Unger, ‘‘A highly accurate firefly based algorithm for heart disease prediction,’’ Expert Syst. Nov. 2015.
Chiba, Z., El Kasmi Alaoui, M. S., Abghour, N., & Moussaid, K. (2022). Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm. International Journal of Communication Networks and Information Security (IJCNIS), 14(1). https://doi.org/10.17762/ijcnis.v14i1.5264 (Original work published April 12, 2022)
G. Guidi, M. C. Pettenati, P. Melillo, and E. Iadanza, “A machine learning system to improve heart failure patient assistance,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 6, pp. 1750–1756, 2014.
Binal A. Thakkar, Mosin I. Hasan, Mansi A. Desai, “Health Care Decision Support System For Swine Flu Prediction Using Naïve Bayes Classifier”, International Conference on Advances in Recent Technologies in Communication and Computing,2010.
Maloth, Bhav Singh. (2016). Privacy-Preserving Scalar Product Computation over Personal Health Records. International Journal of Computer Engineering In Research Trends. 3. 42-46.
(2022). Bug2 algorithm-based data fusion using mobile element for IoT-enabled wireless sensor networks. Measurement: Sensors. 100548. 10.1016/j.measen.2022.100548.
Roy, S. S., Mallik, A., Gulati, R., Obaidat, M. S., & Krishna, P. V. (2017, January). A deep learning based artificial neural network approach for intrusion detection. In International Conference on Mathematics and Computing (pp. 44-53). Springer, Singapore
Turovsky, O. L., Vlasenko, V., Rudenko, N., Golubenko, O., Kitura, O., & Drobyk, O. (2022). Two-Time Procedure for Calculation of Carrier Frequency of Phasomodulated in Communication Systems. International Journal of Communication Networks and Information Security (IJCNIS), 13(3). https://doi.org/10.17762/ijcnis.v13i3.5165 (Original work published December 25, 2021)
Viswanathan, P., & Krishna, P. V. (2013). A joint FED watermarking system using spatial fusion for verifying the security issues of teleradiology. IEEE Journal of Biomedical and Health Informatics, 18(3), 753-764.
Maloth, Bhav Singh & Lakshmi, M & Kumar, Dr & Parashuram, N. (2017). International Journal on Recent and Innovation Trends in Computing and Communication Improved Trial Division Algorithm by Lagrange"s Interpolation Function. International Journal on Recent and Innovation Trends in Computing and Communication. 5. 1227-1231.