| Abstract |
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Most of the decisions in medical diagnosis are taken on the basis of experts’ opinions. In the case of heart diseases, however, the experts’ decisions do not always reach a consensus since the pattern of heart disorders varies considerably among patients. Researchers have been making continuous efforts to detect heart diseases at the primary stages by using different methodologies in order to increase the chances of curing a condition that has one of the highest mortality rates in the world. The three main objectives of this study were to analyze the global impact of heart diseases on the basis of mortality rates, to assess the risk of heart diseases in different age groups, and to discuss the advantages and disadvantages of methodologies that have been used previously for predicting heart disease at the primary stage. The mortality rate due to heart diseases was assessed according to attributes such as age, population group, clinical risk factors, and geographical locations. Different methodologies were analyzed on the basis of results obtained from literature searches in IEEE, Elsevier, Springer, and other publications. The percentage of deaths due to heart diseases increase with age, indicating that the risk of developing heart disease is directly proportional to age. The analysis of various methodological approaches indicated that data mining and the combination of optimization methods can be effective in predicting heart disease at the initial stages. The current data available on heart diseases can help design better frameworks for predicting new cases. The statistics of heart disease-related death shows a worrying trend world-wide. This study concludes that a framework based on hybrid approaches consisting of the combination of classification and clustering methods of data mining, along with biological system inspired algorithms, can prove to be a landmark in the field of heart disease prediction and detection. |
| References |
: |
|
[1]Alarming Statistics from India. http://neocardiabcare.com/alarming-statistics-india.htm. Accessed 26 December 2017.
|
|
[2]Kahramanli H, Allahverdi N. Mining classification rules for liver disorders. International Journal of Mathematics and Computers in Simulation. 2009; 3(1):9-19.
|
| [Google Scholar] |
|
[3]Cardiovascular vascular diseases statistics, WHO. http://www.who.int/cardiovascular_diseases/en/. Accessed 26 December 2017.
|
|
[4]Prabhakaran D, Jeemon P, Roy A. Cardiovascular diseases in India: current epidemiology and future directions. Circulation. 2016; 133(16):1605-20.
|
| [Crossref] |
[Google Scholar] |
|
[5]Kelly BB, Fuster V. Promoting cardiovascular health in the developing world: a critical challenge to achieve global health. National Academies Press; 2010.
|
| [Google Scholar] |
|
[6]Ouyang H. Africas top health challenge: cardiovascular disease. The Atlantic Journal. 2014:17-25.
|
| [Google Scholar] |
|
[7]Cardiovascular disease fact sheet in Australia. https://www.heartfoundation.org.au/about-us/what-we-do/heart-disease-in-australia/cardiovascular-disease-fact-sheet. Accessed 26 December 2017.
|
|
[8]Allender S, Scarborough P, Peto V, Rayner M, Leal J, Luengo-Fernandez R, et al. European cardiovascular disease statistics. European Heart Network, Brussels, England.2008.
|
| [Google Scholar] |
|
[9]Cardiovascular diseases, WHO India. http://www.searo.who.int/india/topics/cardiovascular_diseases/en/. Accessed 26 December 2017.
|
|
[10]Heart Disease in the United States, CDC. https://www.cdc.gov/heartdisease/facts.htm. Accessed 26 December 2017.
|
|
[11]Institute of health metrics and evaluation (IHME). http://ghdx.healthdata.org/. Accessed 26 December 2017.
|
|
[12]Ritchie H and Roser M. Causes of death. Our world in data. https://ourworldindata.org/. Accessed 2 February 2018.
|
|
[13]Global terrorism database (GTD). https://www.start.umd.edu/gtd/. Accessed 26 December 2017.
|
|
[14]Shouman M, Turner T, Stocker R. Using decision tree for diagnosing heart disease patients. In proceedings of the Australasian data mining conference 2011 (pp. 23-30). Australian Computer Society.
|
| [Google Scholar] |
|
[15]Fida B, Nazir M, Naveed N, Akram S. Heart disease classification ensemble optimization using genetic algorithm. In international multitopic conference 2011 (pp. 19-24). IEEE.
|
| [Crossref] |
[Google Scholar] |
|
[16]Elbedwehy MN, Zawbaa HM, Ghali N, Hassanien AE. Detection of heart disease using binary particle swarm optimization. In federated conference on computer science and information systems 2012 (pp. 177-82). IEEE.
|
| [Google Scholar] |
|
[17]Nahar J, Imam T, Tickle KS, Chen YP. Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications. 2013; 40(4):1086-93.
|
| [Crossref] |
[Google Scholar] |
|
[18]Jabbar MA, Deekshatulu BL, Chandra P. Classification of heart disease using k-nearest neighbor and genetic algorithm. Procedia Technology. 2013; 10:85-94.
|
| [Crossref] |
[Google Scholar] |
|
[19]Austin PC, Tu JV, Ho JE, Levy D, Lee DS. Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. Journal of Clinical Epidemiology. 2013; 66(4):398-407.
|
| [Crossref] |
[Google Scholar] |
|
[20]Alizadehsani R, Habibi J, Hosseini MJ, Mashayekhi H, Boghrati R, Ghandeharioun A, et al. A data mining approach for diagnosis of coronary artery disease. Computer Methods and Programs in Biomedicine. 2013; 111(1):52-61.
|
| [Crossref] |
[Google Scholar] |
|
[21]Taneja A. Heart disease prediction system using data mining techniques. Oriental Journal of Computer Science and Technology. 2013; 6(4):457-66.
|
| [Google Scholar] |
|
[22]Bohacik J, Kambhampati C, Davis DN, Cleland JG. Alternating decision tree applied to risk assessment of heart failure patients. Journal of Information Technologies. 2013; 6(2):25-33.
|
| [Google Scholar] |
|
[23]Persi Pamela I, Gayathri P, Jaisankar N. A fuzzy optimization technique for the prediction of coronary heart disease using decision tree. International Journal of Engineering and Technology. 2013; 5(3):2506-14.
|
| [Google Scholar] |
|
[24]Yang JG, Kim JK, Kang UG, Lee YH. Coronary heart disease optimization system on adaptive-network-based fuzzy inference system and linear discriminant analysis (ANFIS-LDA). Personal and Ubiquitous Computing. 2014; 18(6):1351-62.
|
| [Crossref] |
[Google Scholar] |
|
[25]Liu G, Wang L, Wang Q, Zhou G, Wang Y, Jiang Q. A new approach to detect congestive heart failure using short-term heart rate variability measures. PloS One. 2014; 9(4).
|
| [Crossref] |
[Google Scholar] |
|
[26]Easton JF, Stephens CR, Angelova M. Risk factors and prediction of very short term versus short/intermediate term post-stroke mortality: a data mining approach. Computers in Biology and Medicine. 2014; 54:199-210.
|
| [Crossref] |
[Google Scholar] |
|
[27]Masethe HD, Masethe MA. Prediction of heart disease using classification algorithms. In proceedings of the world congress on engineering and computer science 2014 (pp. 22-4).
|
| [Google Scholar] |
|
[28]Zapata-Impata BS, Ruiz-Fernandez D, Monsalve-Torra A. Swarm intelligence applied to the risk evaluation for congenital heart surgery. In international conference of the engineering in medicine and biology society 2015 (pp. 214-7). IEEE.
|
| [Crossref] |
[Google Scholar] |
|
[29]Yang H, Garibaldi JM. A hybrid model for automatic identification of risk factors for heart disease. Journal of Biomedical Informatics. 2015; 58:171-82.
|
| [Crossref] |
[Google Scholar] |
|
[30]El-Bialy R, Salamay MA, Karam OH, Khalifa ME. Feature analysis of coronary artery heart disease data sets. Procedia Computer Science. 2015; 65:459-68.
|
| [Crossref] |
[Google Scholar] |
|
[31]Jonnagaddala J, Liaw ST, Ray P, Kumar M, Chang NW, Dai HJ. Coronary artery disease risk assessment from unstructured electronic health records using text mining. Journal of Biomedical Informatics. 2015; 58:203-10.
|
| [Crossref] |
[Google Scholar] |
|
[32]Ilayaraja M, Meyyappan T. Efficient data mining method to predict the risk of heart diseases through frequent itemsets. Procedia Computer Science. 2015; 70:586-92.
|
| [Crossref] |
[Google Scholar] |
|
[33]Basu Roy S, Teredesai A, Zolfaghar K, Liu R, Hazel D, Newman S, et al. Dynamic hierarchical classification for patient risk-of-readmission. In proceedings of the international conference on knowledge discovery and data mining 2015 (pp. 1691-700). ACM.
|
| [Crossref] |
[Google Scholar] |
|
[34]Sung SF, Hsieh CY, Yang YH, Lin HJ, Chen CH, Chen YW, et al. Developing a stroke severity index based on administrative data was feasible using data mining techniques. Journal of Clinical Epidemiology. 2015; 68(11):1292-300.
|
| [Crossref] |
[Google Scholar] |
|
[35]Paul AK, Shill PC, Rabin MR, Akhand MA. Genetic algorithm based fuzzy decision support system for the diagnosis of heart disease. In international conference on informatics, electronics and vision 2016 (pp. 145-50). IEEE.
|
| [Crossref] |
[Google Scholar] |
|
[36]Kalaiselvi C. Diagnosing of heart diseases using average k-nearest neighbor algorithm of data mining. In international conference on computing for sustainable global development 2016 (pp. 3099-103). IEEE.
|
| [Google Scholar] |
|
[37]Kelwade JP, Salankar SS. An optimal structure of multilayer perceptron using particle swarm optimization for the prediction of cardiac arrhythmias. In international conference on reliability, infocom technologies and optimization (Trends and Future Directions) 2016 (pp. 426-30). IEEE.
|
| [Crossref] |
[Google Scholar] |
|
[38]Singh J, Kamra A, Singh H. Prediction of heart diseases using associative classification. In international conference on wireless networks and embedded systems 2016 (pp. 1-7). IEEE.
|
| [Crossref] |
[Google Scholar] |
|
[39]Kelwade JP, Salankar SS. Prediction of heart abnormalities using particle swarm optimization in radial basis function neural network. In international conference on automatic control and dynamic optimization techniques 2016 (pp. 793-7). IEEE.
|
| [Crossref] |
[Google Scholar] |
|
[40]Sultana M, Haider A, Uddin MS. Analysis of data mining techniques for heart disease prediction. In international conference on electrical engineering and information communication technology 2016 (pp. 1-5). IEEE.
|
| [Crossref] |
[Google Scholar] |
|
[41]Purushottam, Saxena K, Sharma R. Efficient heart disease prediction system. Procedia Computer Science. 2016; 85:962-9.
|
| [Crossref] |
[Google Scholar] |
|
[42]Kavitha R, Kannan E. An efficient framework for heart disease classification using feature extraction and feature selection technique in data mining. In international conference on emerging trends in engineering, technology and science 2016 (pp. 1-5). IEEE.
|
| [Crossref] |
[Google Scholar] |
|
[43]Rajathi S, Radhamani G. Prediction and analysis of rheumatic heart disease using KNN classification with ACO. In international conference on data mining and advanced computing 2016 (pp. 68-73). IEEE.
|
| [Crossref] |
[Google Scholar] |
|
[44]Small AM, Kiss DH, Zlatsin Y, Birtwell DL, Williams H, Guerraty MA, et al. Text mining applied to electronic cardiovascular procedure reports to identify patients with trileaflet aortic stenosis and coronary artery disease. Journal of Biomedical Informatics. 2017; 72:77-84.
|
| [Crossref] |
[Google Scholar] |
|
[45]Tayefi M, Tajfard M, Saffar S, Hanachi P, Amirabadizadeh AR, Esmaeily H, et al. Hs-CRP is strongly associated with coronary heart disease (CHD): a data mining approach using decision tree algorithm. Computer Methods and Programs in Biomedicine. 2017; 141:105-9.
|
| [Crossref] |
[Google Scholar] |
|
[46]Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA. Computer aided decision making for heart disease detection using hybrid neural network-genetic algorithm. Computer Methods and Programs in Biomedicine. 2017; 141:19-26.
|
| [Crossref] |
[Google Scholar] |
|
[47]Kennedy J, Eberhart R. Particle swarm optimization. In proceedings of the international conference on neural networks 1995 (pp. 1942-8).
|
| [Crossref] |
[Google Scholar] |
|
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