Heart Disease Detection Using Predictive Optimization Techniques

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N Satyanandam 1,* Ch. Satyanarayana 2

1. Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, Telangana, India

2. Department of CSE, JNTUK University College of Engineering, Kakinada, Andhra Pradesh, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2019.09.02

Received: 18 May 2019 / Revised: 6 Jun. 2019 / Accepted: 25 Jun. 2019 / Published: 8 Sep. 2019

Index Terms

Heart Disease Analysis, Prediction, Optimised Solutions, Machine Learning Techniques, Severity Detection


Health care is a major research domain needed instantaneous solutions. Due to the digitalization of data in each and every domain it is becoming tedious to store and analysis. So, the demand of proficient algorithms for health care data analysis is also increasing. Predictive analytics is the major demand from the health care community to the computing researches in order to predict and reduce the potential health catastrophes. Parallel research attempts are made to predict the possibilities of the disease on the different health care domains at various regions. However, those attempts are limited and not remarkable to achieve the desired outcomes. Recently, in the field of data analytics; Machine Learning techniques became popular in generating optimized solutions with effective data processing capabilities. Henceforth, this research work considers the heart disease analysis using machine learning techniques to determine the disease severity levels. Experiments are made on UCI heart disease dataset and our results shows 92% accuracy the heart severity detection.

Cite This Paper

N Satyanandam, Ch Satyanarayana, "Heart Disease Detection Using Predictive Optimization Techniques", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.9, pp. 18-24, 2019. DOI: 10.5815/ijigsp.2019.09.02


[1]Kannan R., Vasanthi V., “Machine Learning Algorithms with ROC Curve for Predicting and Diagnosing the Heart Disease”. In: Soft Computing and Medical Bioinformatics (2019). Springer Briefs in Applied Sciences and Technology. Springer, Singapore.

[2]Preventing Chronic Disease: A Vital Investment. World Health Organization Global Report, 2005

[3]Global Burden of Disease. 2004 update (2008). World Health Organization.

[4]Yanwei Xing, “Combination Data Mining Methods with New Medical Data to Predicting Outcome of Coronary  Heart Disease”, IEEE Transactions on Convergence Information Technology, pp(868 – 872), 21-23 Nov. 2007

[5]IBM, Data mining techniques, http://www.ibm.com/devel operworks/opensource/library/ba-data-miningtechniques/index.html?ca=drs-,downloaded on 04 April 2013.

[6]Microsoft Developer Network (MSDN).   http://msdn2.microsoft.com/enus/virtuallabs/aa740409.aspx, 2007.

[7]Glymour C., D. Madigan, D. Pregidon and P.Smyth, “Statistical inference and data mining”, Communication of the ACM, pp: 35-41, 2006.

[8]C. Aflori, M. Craus, “Grid implementation of the Apriori algorithm Advances in Engineering Software”, Volume 38, Issue 5, May 2007, pp. 295-300

[9]Srinivas, K., “Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques”, IEEE Transaction on Computer Science and Education (ICCSE), p(1344 - 1349), 2010.

[10]Shanta kumar, B.Patil,Y.S.Kumaraswamy, “Predictive data mining for medical diagnosis of heart disease prediction” IJCSE Vol .17, 2011

[11]M. Anbarasi et. al. “Enhanced Prediction of Heart Disease with Feature Subset Selection using Genetic Algorithm”, International Journal of Engineering Science and Technology Vol. 2(10), 5370-5376 ,2010

[12]Hnin Wint Khaing, “Data Mining based Fragmentation and Prediction of Medical Data”, IEEE, 2011.

[13]Thuraisingham, B.: “A Primer for Understanding and Applying Data Mining”, IT Professional, 28-31, 2000.

[14]Fausett, Laurene (1994), Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice-Hall, New Jersey, USA.

[15]Liu X, Lu R, Ma J, Chen L. Privacy-preserving patient-centric clinical decision support system on naïve bayesian classification. IEEE Journal of Biomedical and Health Informatics. 2016; 20(2):655–88

[16]Dr. Yashpal Singh, Alok Singh Chauhan, Neural Networks In Data Mining, Journal Of Theoretical And Applied Information Technology, 2016

[17]Vikas Chaurasia, Saurabh Pal, Early Prediction of Heart Diseases Using Data Mining Techniques, Published under Caribbean Journal of Science and Technology, 2013.

[18]N Satyanandam, Dr. Ch Satyanarayana, A New Multilayer Perceptron Model to Detect Heart Disease Severity, International Journal of Scientific & Engineering Research, Volume 7, Issue 12, December-2016 ISSN 2229-5518.

[19]N Satyanandam, Dr. Ch Satyanarayana, Detection of Heart Disease Severity using A Novel Multilayer Perceptron Model: Validation through Major Datasets, Advances in Fuzzy Mathematics. ISSN 0973-533X Volume 12, Number 3 (2017), pp. 333-345 © Research India Publications.