FUTURE PREDICTION OF HEART DISEASE THROUGH EXPLORATORY ANALYSIS OF DATA

Main Article Content

Article Sidebar

Published Aug 23, 2021
Dr T LALITHA

Abstract

This research paper aims to give an in-depth analysis of the healthcare field and data analysis related to healthcare. The Healthcare industry usually generates numerous amounts of data. These data are used for making a decision, so this must be very accurate. In order to identify the errors in the healthcare data, Exploratory Data Analysis (EDA) is proposed in this research. The EDA tries to detect the mistake, find the perfect data, check the errors, and determine the correlation. The most dependent analytical techniques and tools for improving the healthcare performance in the areas of operations, decision making, prediction of disease, etc. In most situations, a complicated combination of pathological and clinical evidence is used to diagnose cardiac disease. Because of this complication, clinical practitioners and scientists are keen to learn more about how to anticipate cardiac disease efficiently and accurately. With the use of the K-means algorithm, the factors that cause heart-related disorders and problems are considered and forecasted in this study. The research is based on publicly available medical information about heart disease. There are 208 entries in this dataset, each with eight characteristics: the patient's age, type of chest discomfort, blood glucose level, BP level, heart rate,  ECG, and so on. The K-means grouping technique as well as visualisation and analytics tools, are utilised to forecast cardiac disease. The proposed model's prediction is more accurate than the other model, according to the results.

How to Cite

Dr T LALITHA. (2021). FUTURE PREDICTION OF HEART DISEASE THROUGH EXPLORATORY ANALYSIS OF DATA. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/121
Abstract 44 |

Article Details

Keywords

Heart disease, cardiac arrest, chest pain, EDA, K-means, data analytics

References
1. "Advances in Electronics, Communication and Computing", Springer Science and Business Media LLC, 2021
2. "Evaluation of Big Data Analytics in Medical Science", International Journal of Engineering and Advanced Technology, 2019
3. "Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019)", Springer Science and Business Media LLC, 2020
4. "Proceedings of ICETIT 2019", Springer Science and Business Media LLC, 2020 23
5. "Proceedings of International Conference on Cognition and Recognition", Springer Science and Business Media LLC, 2018
6. Abdulhamit Subasi, Lejla Bandic, Saeed Mian Qaisar. "Cloud-based health monitoring framework using smart sensors and smartphone", Elsevier BV, 2020
7. Ahmed, M. R., Mahmud, S. H., Hossin, M. A., Jahan, H., & Noori, S. R. H. (2018, December). A cloud based four-tier architecture for early detection of heart disease with machine learning algorithms. In 2018 IEEE 4th International Conference on Computer and Communications (ICCC) (pp. 1951-1955). IEEE.
8. Amogh Powar, Seema Shilvant, Varsha Pawar, Vishesh Parab, Pratiksha Shetgaonkar, Shailendra Aswale. "Data Mining & Artificial Intelligence Techniques for Prediction of Heart Disorders: A Survey", 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), 2019
9. Bharti, Sana, and Shaliendra Narayan Singh. "Analytical study of heart disease prediction comparing with different algorithms", International Conference on Computing Communication & Automation, 2015.
10. C. Sowmiya, P. Sumitra. "A hybrid approach for mortality prediction for heart patients using ACO-HKNN", Journal of Ambient Intelligence and Humanized Computing, 2020
11. Data Intelligence and Cognitive Informatics, Springer Science and Business Media LLC,2021
Exploratory Data Analysis", Procedia Computer Science, 2020
12. Gandhi, M., & Singh, S. N. (2015, February). Predictions in heart disease using techniques of data mining. In 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE) (pp. 520-525). IEEE.
13. Hua Ang, J.. "Interference-less neural network training", Neurocomputing, 200810
14. Indhumathi, S., & Vijaybaskar, G. (2015). Web based health care detection using naive Bayes algorithm. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 4(9), 3532-36.
15. Jalal, M. (2019). Performance Evaluation of Machine Learning Algorithms for Coronary Artery Disease Features (Doctoral dissertation, United International University).
16. Malav, A., Kadam, K., & Kamat, P. (2017). Prediction of heart disease using k-means and artificial neural network as hybrid approach to improve accuracy. International Journal of Engineering and Technology, 9(4), 3081-3085.
17. Mohammadi, N., Aghayousefi, A., Nikrahan, G. R., Adams, C. N., Alipour, A., Sadeghi, M., & Huffman, J. C. (2018). A randomized trial of an optimism training intervention in patients with heart disease. General hospital psychiatry, 51, 46-53.
18. Musunuru, K., & Kathiresan, S. (2019). Genetics of common, complex coronary artery disease. Cell, 177(1), 132-145.
19. Palaniappan, S., & Awang, R. (2008, March). Intelligent heart disease prediction system using data mining techniques. In 2008 IEEE/ACS international conference on computer systems and applications (pp. 108-115). IEEE.
20. Pooja Rani, Rajneesh Kumar, Nada M. O. Sid Ahmed, Anurag Jain. "A decision support system for heart disease prediction based upon machine learning", Journal of Reliable Intelligent Environments, 2021
Section
GE3- Computers & Information Technology