Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Data Mining Application for Health Seeker and Provider

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Parul Berwal, Kamna Solanki

Parul Berwal and Kamna Solanki. Data Mining Application for Health Seeker and Provider. International Journal of Computer Applications 149(8):15-23, September 2016. BibTeX

	author = {Parul Berwal and Kamna Solanki},
	title = {Data Mining Application for Health Seeker and Provider},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2016},
	volume = {149},
	number = {8},
	month = {Sep},
	year = {2016},
	issn = {0975-8887},
	pages = {15-23},
	numpages = {9},
	url = {},
	doi = {10.5120/ijca2016911525},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Electro Cardio gram is the technique that is utilized to calculate the occurrence and consistency of heart beat. By distinguishing the overall ECG signal, doctors can easily predict that the signal is disposed to to heart attack or not. The signal processing is functioning by the computer based analysis which takes form of the alteration of the signal into another and this signal so generated is more desired than original. This research helps to identify the signal is prone to heart attack or not. This comprises the choice of some basic characteristic and comparing the neural networks outcomes with a hybrid method of ANN and FL (neural –fuzzy classifier). The outcomes so acquired after the effective comparison of each classifier states that ANFIS categorizes more perfectly than the Neural Networks.


  1. Periyanga. J1 , Preethi. B2 , Priya. M3 , Ramakrishanan,’ Instant Answering For Health Seekers Using Machine Learning’, IJSRSET,2015.
  3. Graja, S. and Boucher, J.M. 2005, ‘Hidden Markov Tree Model Applied to ECG Delineation’, IEEE Transactions on Instrumentation and Measurement, vol. 54, no. 6.
  4. Jager F. 2002, ‘Feature Extraction and Shape Representation of Ambulatory Electrocardiogram Using the Karhunen-Lo`eve Transform’, Electrotechnical Review, Ljubljana, Slovenija.
  5. Gao, D., Madden, M., Schukat, M., Chambers, D. and Lyons, G. 2004, ‘Arrhythmia Identification from ECG Signals with a Neural Network Classifier Based on a Bayesian Framework’, Department of Information Technology, National University of IrelandTinati, M.A. and Mozaffary B. 2006,‘A Wavelet Packets Approach to Electrocardiograph Baseline Drift Cancellation’, International Journal of Biomedical Imaging, vol. 10, pp. 1-9.
  6. Nobuo Ezaki, Marius Bulacu Lambert , Schomaker , “Text Detection from Natural Scene Images: Towards a System for Visually Impaired Persons” , Proc. of 17th Int. Conf. on Pattern Recognition (ICPR), IEEE Computer Society, pp. 683-686, vol. II, 2004.
  7. Herrero, G.G., Krasteva, V., Christov, I., Jekova, I., Gotchev, A. and Egiazarian, K. 2006, ‘Comparative Study Of Morphological And Time-Frequency ECG Descriptors For Heartbeat Classification’, Medical Engineering and Physics, vol. 28, issue 9, pp. 876-887.
  8. Ahmadian, A., Karimifard, S. , Sadoughi, H. and Abdoli, M. 2007, ‘An Efficient Piecewise Modeling of ECG Signals Based on Hermitian Basis Functions’, Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, France, pp. 3180-3183.


Electrocardiogram, Neural- Fuzzy, Neural Network, ANFIS