Main Article Content

Article Sidebar

Published Sep 14, 2021


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, & DIDWANIA, R. (2021). FUTURE PREDICTION OF HEART DISEASE THROUGH EXPLORATORY ANALYSIS OF DATA. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/324
Abstract 5 |

Article Details

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, 202023
5. "Proceedings of International Conference on Cognition and Recognition", Springer Science and Business Media LLC, 2018
6. AbdulhamitSubasi, LejlaBandic, SaeedMianQaisar. "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. AmoghPowar, SeemaShilvant, VarshaPawar, VisheshParab, PratikshaShetgaonkar, ShailendraAswale. "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. HuaAng, 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
21. 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
22. R. Indrakumari, T. Poongodi, SoumyaRanjan Jena. "Heart Disease Prediction using Exploratory Data Analysis", Procedia Computer Science, 2020
23. Rahul Katarya, Sunit Kumar Meena. "Machine Learning Techniques for Heart Disease Prediction: A Comparative Study and Analysis", Health and Technology, 2020
24. RitikaMehra, MohitIyer. "chapter 8 AI-Driven Prognosis and Diagnosis for Personalized Healthcare Services", IGI Global, 2020
25. SabahattinIsik, Vijay P. Singh. "Hydrologic Regionalization of Watersheds in Turkey", Journal of Hydrologic Engineering, 2008
26. Salavati, H., Gandomani, T. J., &Sadeghi, R. (2017, September). A robust software architecture based on distributed systems in big data healthcare. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1701-1705). IEEE. 1
27. Samira Akhbarifar, Hamid Haj SeyyedJavadi, Amir MasoudRahmani, Mehdi Hosseinzadeh."A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment", Personal and Ubiquitous Computing, 2020
28. Shamrat, F. J. M., Raihan, M. A., Rahman, A. S., Mahmud, I., &Akter, R. (2020). An Analysis on Breast Disease Prediction Using Machine Learning Approaches. International Journal of Scientific & Technology Research, 9(02), 2450-2455.
29. Singh, P., Singh, S., &Pandi-Jain, G. S. (2018). Effective heart disease prediction system using data mining techniques. International journal of nanomedicine, 13(T-NANO 2014 Abstracts), 121.
30. TapabrataRay, Warren Smith. "A surrogate assisted parallel multiobjective evolutionary algorithm for robust engineering design", Engineering Optimization, 2006
31. Tarun, A. S. D. T. D., & Gupta, K. Comparative Analysis of Machine Learning Techniques in Heart Disease Prediction by R Language.
32. Thomas, J., &Princy, R. T. (2016, March). Human heart disease prediction system using data mining techniques. In 2016 international conference on circuit, power and computing technologies (ICCPCT) (pp. 1-5). IEEE.
33. Udhan, S., &Patil, B. (2021). A systematic review of Machine learning techniques for Heart disease prediction. International Journal of Next-Generation Computing, 12(2).
34. Venkatesan, C., Karthigaikumar, P., &Satheeskumaran, S. (2018). Mobile cloud computing for ECG telemonitoring and real-time coronary heart disease risk detection. Biomedical Signal Processing and Control, 44, 138-145.
35. Verbeeck, N., Caprioli, R. M., & Van de Plas, R. (2020). Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry. Mass spectrometry reviews, 39(3), 245-291.
36. Watkins, D. A., Beaton, A. Z., Carapetis, J. R., Karthikeyan, G., Mayosi, B. M., Wyber, R., ... &Zühlke, L. J. (2018). Rheumatic heart disease worldwide: JACC scientific expert panel. Journal of the American College of Cardiology, 72(12), 1397-1416.31
37. www.cinc.org
38. www.growingscience.com
39. www.ijitee.org
40. www.news24.com
GE3- Computers & Information Technology