Survey on Heart Disease Prediction System Based on Data Mining Techniques
Objectives: To be familiar with the kinds of coronary illness, and information mining procedures to fight them.
Methods/Statistical analysis: To handle this, data mining concepts and techniques used were discussed to discover hidden patterns from medical domain.
Findings: The purpose of predictions in data mining is to discover trends in patient data through patterns generation to improve the health strategy. The algorithms presented here are with a specific end goal to anticipate the coronary illness which includes some constraint.
- Kimio Tanaka, Natarajan Gajendran, Hideki Asaoku, TaichiKyo, NanaoKamada. Higher involvement of subtelomere regions for chromosome rearrangements in leukemia and lymphoma and in irradiated leukemic cell line. Indian Journal of Science and Technology. 2012; 5(1), 1801-1811.
- S. Akila, S. Chandramathi. A hybrid method for coronary heart disease risk prediction using decision tree and multi layer perceptron. Indian Journal of Science and Technology. 2015; 8(34), 1-7.
- Carlos Ordonez. Association rule discover with the train and test approach for the heart disease prediction. IEEE Transactions on Information Technology in Biomedicine. 2006; 10(2), 334-343.
- Chang-Sik Son, Yoon-Nyun Kim. Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches. Journal of Biomedical Informatics.2012; 45, 999-1008.
- DorairajPrabhakaran, Panniyammakal, Jeemon, Ambuj Roy. Cardiovascular diseases in India. Current Epidemiology and Future Directions. 2016; 133(16), 1605-1619.
- P. Gayathri, N. Jaisankar. Comprehensive study of heart disease diagnosis using data mining and soft computing techniques. International Journal of Engineering and Technology.2013; 5(3), 2947-2958.
- Goekmen R. Turan, I. Bozdag. Improved functional activity of bone marrow derived circulating progenitor cells after intra coronary freshly isolated bone marrow cells transplantation in patients with ischemic heart disease.Journal of stem cell review and report.2011; 7, 646-656.
- Hongmei Yan, Jun Zheng. Selecting critical clinical features for heart diseases diagnosis with a real-coded genetic algorithm. Journal of Applied Soft Computing. 2008; 8(2), 1105-1111.
- JesminNahar, Tasadduq Imam. Association rule mining to detect factors which contribute to heart disease in males and females. Journal of Expert Systems with Applications. 2013; 40(4), 1086–1093.
- Kemal Polat, Salih Gunes. A new feature selection method on classification of medical datasets: Kernel F-score feature selection. Journal of Expert Systems with Applications. 2009; 36(7), 10367–10373.
- Latha Parthiban, R. Subramanian. Intelligent heart disease prediction system using CANFIS and genetic algorithm. International Journal of Biological and Life Science.2007; 15, 157- 160.
- Lucile Houyel, Babak Khoshnood. Population-based evaluation of a suggested anatomic and clinical classification of congenital heart defects based on the International Paediatric and Congenital Cardiac Code. International Journal of rare diseases. 2011; 64(6).
- S. Muthukaruppan, M.J. Er. A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease. Journal of Expert Systems with Applications.2012; 39(14), 11657–11665.
- OlatubosunOlabode, Bola Titilayo Olabode. Cerebrovascular accident attack classification using multilayer feed forward artificial neural network with back propagation error. Journal of Computer Science.2012; 8(1), 18-25.
- PasiLuukka, Jouni Lampinen. A classification method based on principal component analysis and differential evolution algorithm applied for prediction diagnosis from clinical EMR heart data sets. Journal of computer intelligence in optimization adaption, learning and optimization.2010; 7, 263-283.
- Peter C. Austin, Jack V. Tu. 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.
- Petra A. Karsdorp, MerelKindt. False heart rate feedback and the perception of heart symptoms in patients with congenital heart disease and anxiety. International Journal of behavioral Medicine.2009; 16(1), 81-88.
- R. Pfister, D. Barnes, R.N. Luben, K.T. Khaw, N.J. Wareham, C. Langenberg. Individual and cumulative effect of type 2 diabetes genetic susceptibility variants on risk of coronary heart disease. Journal of Diabetologia.2011; 54(9), 2283-2287.
- K. Rajeswari, V. Vaithiyanathan. Feature selection in ischemic heart disease identification using feed forward neural networks. International Symposium on Robotics and Intelligent Sensors. 2012; 41, 1818–1823.
- Resul Das, Ibrahim Turkoglu. Diagnosis of valvular heart disease through neural networks ensembles. Journal of Computer Methods and Programs in Biomedicine.2009; 93, 185-191.
- N.A. Setiawan. A comparative study of imputation methods to predict missing attributes values in coronary heart disease data set.Journal in Department of Electrical and Electronic Engineering. 2008; 21, 266–269.
- J.Senthil Kumar, S.Appavu. The personalized disease prediction care from harm using big data analytics in healthcare. Indian Journal of Science and Technology. 2016; 9(8), 1-6.
- VahidKhatibi, Gholam Ali Montazer. A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment. Journal of Expert Systems with Applications. 2010; 37(12), 8536–8542.
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