Open Access Open Access  Restricted Access Subscription or Fee Access

Lloyd and Minkowski Based K-Means Clustering for Effective Diagnosis of Heart Disease and Stroke


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecos.v10i6.6265

Abstract


Computer Aided Data mining based decision support system plays a major role in research to easily diagnose medical disease at an early stage. Automatic annotation approach grouped similar medical semantic terms, however, false detection lead to incorrect merging increasing the computational complexity. In this work, a method called Lloyd and Minkowski based K-Means Clustering (LMK-MC) is designed to organize various features in heart disease, with the aim of increasing the features to be incorporated improving the clustering efficiency and reducing the average computational complexity. The Lloyd and Minkowski based K-Means Clustering includes a function that performs a Minkowski based K-Means Clustering to organize similar type of symptom features for easy prediction. By applying Minkowski based K-Means Clustering, similar type of features causing heart disease is analyzed for any set of k centers, aiming at improving the clustering efficiency. Next, the method LMK-MC, applies pair-wise proximities between all pairs of features reducing the computational complexity for intra-cluster. Finally, Lloyd's algorithm based clustering on medical data is designed that moves every center point to the centroid and performs updates. Lloyd's algorithm based clustering obtains local minima solution that easily merges similar features leading to heart disease and strokes, various disease features for labeling is identified effectively with high recognition accuracy. In order to measure the efficiency of LMK-MC, experiments are conducted using Cleveland Clinic Foundation Heart disease data set. Experimental results demonstrate that the proposed Lloyd and Minkowski based K-Means Clustering achieves efficient amount of identification improving the clustering efficiency, reducing the computational complexity while measuring intra-cluster distances for different clusters and, heart disease and stroke identification rate with minimum processing time.
Copyright © 2015 Praise Worthy Prize - All rights reserved.

Keywords


Lloyd’s Algorithm; Minkowski Based K-Means Clustering; Pair-wise Proximities; Centroid

Full Text:

PDF


References


Yiyao Lu, Hai He, Hongkun Zhao, Weiyi Meng, and Clement Yu, Annotating Search Results from Web Databases, IEEE Transactions on Knowledge and Data Engineering, Vol. 25, No. 3, March 2013, Pages: 514 – 527.
http://dx.doi.org/10.1109/tkde.2011.175

Luis A. Leiva, Enrique Vidal, Warped K-Means: An algorithm to cluster sequentially-distributed data, Information Sciences., Elsevier journal, Vol. 237, 2013, Pages: 196 – 210.
http://dx.doi.org/10.1016/j.ins.2013.02.042

Nguyen, T. P. L., Schuiling-Veninga, N., Nguyen, T. B. Y., Hang, V. T. T., Wright, E. P., & Postma, M. J. (2014). Models to Predict the Burden of Cardiovascular Disease Risk in a Rural Mountainous Region of Vietnam. Value in Health Regional Issues, 3(1), 87-93.
http://dx.doi.org/10.1016/j.vhri.2014.03.003

M. Akhil Jabbar, B.L Deekshatulu & Priti Chandra, Classification of Heart Disease using Artificial Neural Network and Feature Subset Selection, Global Journal of Computer Science and Technology Neural & Artificial Intelligence, Volume 13 Issue 3, Version 1.0, June 2013

Manuela Fiuza, Metabolic syndrome and coronary artery disease, Portuguese Journal of Cardiology, June 2012, 779-782.
http://dx.doi.org/10.1016/j.repce.2012.10.004

Oleg Yu. Atkov, Svetlana G. Gorokhova, Alexandr G. Sboev, Eduard V. Generozov, Elena V. Muraseyeva, Svetlana Y. Moroshkina, Nadezhda N. Cherniy, Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters, Journal of Cardiology, Elsevier, Vol. 59, Issue 2, May 2012, Pages 190 – 194.
http://dx.doi.org/10.1016/j.jjcc.2011.11.005

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, Elsevier, Vol 24, Issue 1, 2012, Pages 27-40.
http://dx.doi.org/10.1016/j.jksuci.2011.09.002

Manisha Barman, J Paul Chaudhury, A Framework for Selection of Membership Function Using Fuzzy Rule Base System for the Diagnosis of Heart Disease, I.J. Information Technology and Computer Science, Sep 2013, Pages : 62-70.
http://dx.doi.org/10.5815/ijitcs.2013.11.07

Tin Tin Su, Mohammadreza Amiri, FarizahMohd Hairi, Nithiah Thangiah, Awang Bulgiba, and Hazreen AbdulMajid, Prediction of Cardiovascular Disease Risk among Low-Income Urban Dwellers in Metropolitan Kuala Lumpur, Malaysia, Hindawi Publishing Corporation BioMed Research International Volume 2015
http://dx.doi.org/10.1155/2015/516984

Jamal Salahaldeen Majeed Alneamy and Rahma Abdulwahid Hameed Alnaish, Heart Disease Diagnosis Utilizing Hybrid Fuzzy Wavelet Neural Network and Teaching Learning Based Optimization Algorithm, Hindawi Publishing Corporation Advances in Artificial Neural Systems Volume 2014
http://dx.doi.org/10.1155/2014/796323

Durairaj. M, Sivagowry. S, Feature Diminution by Using Particle Swarm Optimization for Envisaging the Heart Syndrome, I.J. Information Technology and Computer Science, Vol. 7, No. 2, Jan 2015.
http://dx.doi.org/10.5815/ijitcs.2015.02.05

Boshra Bahrami, Mirsaeid Hosseini Shirvani, Prediction and Diagnosis of Heart Disease by Data Mining Techniques, Journal of Multidisciplinary Engineering Science and Technology (JMEST), Vol. 2 Issue 2, February 2015.

Umair Shafique, Fiaz Majeed, Haseeb Qaiser, and Irfan Ul Mustafa, Data Mining in Healthcare for Heart Diseases, International Journal of Innovation and Applied Studies ISSN 2028-9324 Vol. 10 No. 4 Mar 2015.

Atul Kumar Pandey,Prabhat Pandey,K.L. Jaiswal,Ashish Kumar S, A Heart Disease Prediction Model using Decision Tree, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 12, Issue 6 (Jul. - Aug. 2013).

Jennifer L. Bolton, Marlene C. W. Stewart, James F. Wilson, Niall Anderson, Jackie F. Price, Improvement in Prediction of Coronary Heart Disease Risk over Conventional Risk Factors Using SNPs Identified in Genome-Wide Association Studies, PLOS ONE, February 2013 | Volume 8 | Issue 2
http://dx.doi.org/10.1371/journal.pone.0057310

Jyoti Soni, Ujma Ansari Dipesh Sharma, Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction, International Journal of Computer Applications (0975 – 8887) Volume 17– No.8, March 2011
http://dx.doi.org/10.5120/2237-2860

Lennie Samsell, Michael Regier, Cheryl Walton, and Lesley Cottrell, Importance of Android/Gynoid Fat Ratio in Predicting Metabolic and Cardiovascular Disease Risk in Normal Weight as well as Overweight and Obese Children, Hindawi Publishing Corporation Journal of Obesity Volume 2014
http://dx.doi.org/10.1155/2014/846578

Hlaudi Daniel Masethe, Mosima Anna Masethe, Prediction of Heart Disease using Classification Algorithms, Proceedings of the World Congress on Engineering and Computer Science 2014 Vol II WCECS 2014, 22-24 October, 2014
http://dx.doi.org/10.1007/978-94-017-9115-1_26

Abhishek Taneja, Heart Disease Prediction System Using Data Mining Techniques, Oriental Journal of Computer Science & Technology, Vol. 6, No. (4), Pgs. 457-466, December 2013,

Vikas Chaurasia, Saurabh Pal, Early Prediction of Heart Diseases Using Data Mining Techniques, Caribbean Journal of Science and Technology, Vol.1, 208-217, May 2013.

UCI (University of California, Irvine C.A) Machine Learning Repository, 2014 [online] available: http://repository.seasr.org/Datasets/UCI/arff/ (June 1, 2014).

Saravana Kumar, R., Tholkappia Arasu, G., A fast K-Modes Clustering Algorithm to warehouse very large heterogeneous medical databases, (2013) International Review on Computers and Software (IRECOS), 8 (6), pp. 1476-1488.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize