Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Kanimozhi, N.
- An Effective Heart Disease Prediction Using Machine Learning Technique
Abstract Views :202 |
PDF Views:1
Authors
Affiliations
1 Department of Information Technology, Sona College of Technology, IN
1 Department of Information Technology, Sona College of Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 3 (2021), Pagination: 2323-2327Abstract
Heart disease is the foremost significant causes of transience within the world nowadays. It is a vital challenge to predict the cardiovascular disease in the range of clinical data investigation. Machine Learning (ML) is the most popular and powerful approach that has been appeared to be effective in making decisions and predictions from the huge amount of information delivered by the healthcare industry. ML techniques are also used in recent developments in wide areas of the Internet of Things (IoT). There are various studies done to predict the heart disease with ML techniques and it gives only a glimpse of it. In this paper, a simple TensorFlow model is proposed to find out major features by applying ML techniques that result in better accuracy in the prediction of cardiovascular disease. The prediction model is presented with diverse combinations of features and known classification algorithms. This version for coronary heart disorder with the ML based TensorFlow Model produces a more desirable overall performance with a higher accuracy stage in prediction.Keywords
Machine Learning, Heart Disease Prediction, Feature Selection, Binary Classification, Tensorflow.
References
- P.K. Anooj, “Clinical Decision Support System: Risk Level Prediction of Heart Disease using Weighted Fuzzy Rules”, Journal of King Saud University - Computer and Information Sciences, Vol. 24, No. 1, pp. 27-40, 2012.
- M. Durairaj and V. Revathi, “Prediction of Heart Disease using Back Propagation MLP Algorithm”, International Journal of Scientific and Technology, Vol. 4, No. 8, pp. 235-239, 2015
- M. Gandhi, “Predictions in Heart Disease using Techniques of Data Mining”, Proceedings of International Conference on Futuristic Trends on Computational Analysis and Knowledge Management, pp.520-525, 2015.
- A. Gavhane, “Prediction of Heart Disease using Machine Learning”, Proceedings of International Conference on Electronics, Communication and Aerospace Technology, pp.1275-1278, 2018.
- P.S. Kumar, “A Computational Intelligence Method for Effective Diagnosis of Heart Disease using Genetic Algorithm”, International Journal of Bio-Science and Bio-Technology, Vol. 8, No. 2, pp. 363-372, 2016.
- J. Nahar and T. Imam, “Computational Intelligence for Heart Disease Diagnosis: A Medical Knowledge Driven Approach”, Expert Systems with Applications, Vol. 40, No. 1, pp. 96-104, 2013.
- J. Nahar and T. Imam, “Association Rule Mining to Detect Factors which Contribute to Heart Disease in Males and Females”, Expert Systems with Applications, Vol. 40, No. 4, pp. 1086-1093, 2013.
- Tulay Karaylan and Ozkan Kilic, “Prediction of Heart Disease using Neural Network”, Proceedings of International Conference on Computer Science and Engineering, pp. 719-723, 2017.
- T.P. Thomas, “Human Heart Disease Prediction System using Data Mining Techniques”, Proceedings of International Conference on Circuit, Power and Computing Technologies, pp. 12-18, 2016.
- D.K. Ravish and N.R. Shenoy, “Heart Function Monitoring, Prediction and Prevention of Heart Attacks: using Artificial Neural Networks”, Proceedings of International Conference on Contemporary Computing and Informatics, pp. 1-6, 2014.
- Y.E. Shao, C. Hou and C. Chiu, “Hybrid Intelligent Modeling Schemes for Heart Disease Classification”, Applied Soft Computing, Vol. 14, No. 1, pp.47-52, 2014.
- Senthilkumar Mohan, “Effective Heart Disease Prediction using hybrid Machine Learning Techniques”, IEEE Access, Vol. 7, pp. 81542-81554, 2016.
- C.L. Blake and C.J. Merz, “UCI Repository of Machine Learning Databases”, Available at http://www.ics.uci.edu/~ mlearn/MLRepository.html, Accessed at 1998.
- P. Shanmugaraja, K. Chokkanathan, J. Anitha, A. Parveen Begam and N. Naveenkumar, “Dynamic Packet Scheduler for Queuing Real Time and Non-Real Time Internet Traffic”, International Journal of Recent Technology and Engineering, Vol. 8, No. 3, pp. 1-12, 2019.
- J. Akilandeswari and G. Jothi, “Performance Comparison of Machine Learning Algorithms that Predicts Students’ Employability”, Proceedings of International Conference on Information and Communication Systems, pp. 221-228, 2017.
- L. Sathish Kumar and A. Padmapriya, “Prediction for Common Disease using ID3 Algorithm in Mobile Phone and Television”, International Journal of Computer Applications, Vol. 50, No. 4, pp. 23-29, 2012.
- Wiharto Wiharto and Hari Kusnanto, “Intelligent Systems for Diagnosis Level of Coronary Heart Disease with K-Star Algorithm”, Healthcare Informatics Research, Vol. 22, No. 1, pp. 1-15, 2016.
- S. Jayashril and D.R.P. Sonawane, “Prediction of Heart Disease using Learning Vector Quantization Algorithm”, Proceedings of International Conference on Business, Industry and Government, pp. 451-456, 2014.
- S. Nandhini and Monojit Debnath, “Heart Disease Prediction using Machine Learning”, International Journal of Recent Engineering and Research and Development, Vol. 3, No. 10, pp. 39-46, 2018.
- Devansh Shah and Samir Patel, “Heart Disease prediction using Machine Learning Techniques”, SN Computer Science, Vol. 1, No. 6, pp. 1-13, 2020.