IJBTT

Heart Disease Prediction System using Data Mining Method

	International Journal of Engineering Trends and Technology (IJETT)
	© 2017 by IJETT Journal
	Volume-47 Number-6
	Year of Publication : 2017
	Authors : Keerthana T K
	DOI :  10.14445/22315381/IJETT-V47P259

Citation 

Keerthana T K "Heart Disease Prediction System using Data Mining Method", International Journal of Engineering Trends and Technology (IJETT), V47(6),361-363 May 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Heart disease is most common in present era. The treatment cost of heart disease is not affordable by most of the patients. So we can reduce this problem by a Heart Disease Prediction System (HDPS).It is helpful for earlier diagnosis of heart disease. Data mining techniques are used for the construction of HDPS. In health care field some systems use large healthcare data in varied forms such as images, texts, charts and numbers .But this datas are hardly visited and are not mined. This problem can be avoided by introducing HDPS. This system would enhance medical care and it can also reduce the costs. The system can handle complex queries for detection of heart disease and thus help to make intelligent medical decisions. This paper proposes a HDPS based on three different data mining techniques. The various data mining methods used are Naive Bayes, Decision tree (J48), Random Forest and WEKA API. The system can predict the likelihood of patients getting a heart disease by using medical profiles such as age, sex, blood pressure, cholesterol and blood sugar. Also, the performance will be compared by calculation of confusion matrix. This can help to calculate accuracy, precision, and recall. The overall system provides high performance and better accuracy.

 References

[1] Ankita Dewan and Meghna Sharma "Prediction of Heart Disease Using a Hybrid Technique in Data Mining ClassifIcation" IEEE2010
[2] Majali J, Niranjan R, Phatak V,Tadakhe O.Data mining techniques for diagnosis and prognosis of cancer. International Journal of Advanced Researchin Computer and Communication Engineering.2015;4(3):613–6.
[3] Ms.Rupali R.Patil "Heart Disease Prediction System using Naïve Bayes and Jelinek-mercer smoothing" IJARCCE 2014
[4] Monika Gandhi and Dr. Shailendra Narayan Singh "Predictions in Heart Disease Using Techniques of Data Mining" International Conference Conference on Futuristic trend in ComputationalAnalysis and Knowledge Management (ABLAZE-2015)
[5] Bhuvaneswari Amma N.G.," Cardiovascular Disease Prediction System using Genetic Algorithm and Neural Network" 2014
[6] Chaitrali S. Dangare et. al.,“Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques”, (IJCA) (0975 – 8887), Vol. 47, No. 10, June 2012, page no. 44-48.
[7] S. Vijiyarani et. al., “An Efficient Classification Tree Technique for Heart Disease Prediction”, (ICRTCT - 2013) Proceedings published in (IJCA) (0975 – 8887), 2013, page no. 6-9.
[8] Y.E.Shao, C.-D. Hou, and C.-C. Chiu,”Hybrid intelligent modelling schemes for heart disease classification, ”Applied Soft Computing, vol. 14,pp. 47–52, 2014.
[9] Y. Xing, J. Wang, Z. Zhao, and Y. Gao, “Combination data mining methods with new medical data to predicting outcome of coronary heart disease,” pp. 868–872, 2007.
[10] Guru, N., Anil, D., Navin, R., Decision Support System FoR Heart Disease Diagnosis Using Neural Network.Delhi Business Review. 8(1): (2007).
[11] Shouman M, Turner T, Stocker R. Using decision tree for diagnosing heart disease patients. Proceedings of the 9th Australasian Data Mining Conference(AusDM’11); Ballarat, Australia. 2011. p. 23–30.
[12] RaniKU.Analysis of heart diseases dataset using neural network approach.IJDKP. 2011; 1(5):1–8.

Keywords
HDPS; WEKA; Random Forest; Naïve Bayes; J48.