International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 11, No. 4, 8 Jul. 2019
Cover page and Table of Contents: PDF (size: 551KB)
A Survey on Risk Assessments of Heart Attack Using Data Mining Approaches
Full Text (PDF, 551KB), PP.43-51
Views: 0 Downloads: 0
Author(s)
Yogita Solanki
1,*
Sanjiv Sharma
1
Index Terms
Medical Data Mining, Machine Learning algorithm, Heart Disease Prediction, Heart Disease, Comprehensive Review.
Abstract
This document presents the required layout of articles to Medical data mining has become one of the prominent issues in the field of data mining due to the delicate lifestyle opted by the people which are leading them towards various chronicle health diseases. Heart disease is one of the conspicuous public health concern worldwide issues. Since clinical data is growing rapidly owing to deficient health awareness, various techniques and scientific methods are opted for analyzing this huge data. Several data mining techniques such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision tree, Naïve Bayes and Artificial Neural Network (ANN) are introduced for the prediction of health disease. These techniques help to mine the relevant and useful amount of data, form the medical dataset which helps to provide beneficial information to the medical institutions. This study presents various issues related to healthcare and various machines learning algorithms which have to withstand to provide the best possible output. A comprehensive review of the literature has been summarized to put lights on the previous work done in this field.
Cite This Paper
Yogita Solanki, Sanjiv Sharma, "A Survey on Risk Assessments of Heart Attack Using Data Mining Approaches", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.11, No.4, pp. 43-51, 2019. DOI:10.5815/ijieeb.2019.04.05
Reference
[1]Susmitha K, B. Senthi Kumar, "A Survey on Data Mining Techniques for Prediction of Heart Diseases", IOSR Journal of Engineering (IOSRJEN), Vol. 08, Issue 9 (September. 2018), pp. 22-27.
[2]Animesh Hazra, Subrata Kumar Mandal, Amit Gupta, Arkomita Mukherjee, and Asmita Mukherjee , "Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques: A Review", Advances in Computational Sciences and Technology ISSN 0973-6107 Volume 10, Number 7 (2017), pp. 2137-2159
[3]Abhishek Bajpai, and Dr. Sanjiv Sharma. (2018). "BIG DATA ANALYSIS IN HEALTH CARE DOMAIN: A SYSTEMATIC REVIEW." International Journal of Engineering Technologies and Management Research, 5(2: SE), pp. 1-8. DOI: 10.5281/zenodo.1195065.
[4]K.Gomathi, Dr. D. Shanmuga Priyaa, “A Survey On Heart Disease Prediction Using Data Mining Techniques”, International Journal Of Multidisciplinary Educational Research May 2016, pp. 56-62.
[5]Amruta Powar, Prof. Dr. Vijay Ghorpade, “Survey On Heart Disease Prediction System Using Data Mining Technique”, International Journal Of Innovations In Engineering Research And Technology [IJIERT] Oct.-2017, pp. 25-28.
[6]Rucha Shinde, Sandhya Arjun, Priyanka Patil, Prof. Jaishree Waghmare, “An Intelligent Heart Disease Prediction System Using K-Means Clustering and Naïve Bayes Algorithm”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (1), 2015, pp. 637-639.
[7]Mirpouya Mirmozaffari, Alireza Alinezhad, and Azadeh Gilanpour, “Heart Disease Prediction with Data Mining Clustering Algorithms”, Int'l Journal of Computing, Communications & Instrumentation Engg. (IJCCIE) Vol. 4, Issue 1 2017 pp. 16-19.DOI: https://doi.org/10.15242/IJCCIE.DIR1116009.
[8]Lakshmishree J, K Paramesha,” Prediction of Heart Disease Based on Decision Trees”, International Journal for Research in Applied Science & Engineering Technology (IJRASET) Volume 5 Issue V, May 2017 pp. 943-948.
[9]Ajay Kumar Mishra, Bikram Kesari Ratha, "Study of the random tree and random Forest data mining algorithms for microarray data analysis, International journal on advanced electrical and computer engineering (IJAECE), ISSN (Print): 2349-932X Volume-3, Issue-4, 2016, pp. 5-7.
[10]Ms. Shikha Sharma, Mr. Chetan Chauhan, et al., "Improve the Accuracy of ID3 Classifier for Heart Disease Prediction”, International Journal for Rapid Research in Engineering Technology & Applied Science ISSN (Online): 2455-4723 Vol. 3 Issue 5 May 2017.
[11]Wiharto, W., Kusnanto, H., & Herianto, H., “Interpretation of Clinical Data Based on C4.5 Algorithm for the Diagnosis of Coronary Heart Disease”, Healthcare Informatics Research, (2016) Vol. 22, pp. 186-195. DOI: 10.4258/hir.2016.22.3.186
[12]Animesh Hazra, Subrata Kumar Mandal, Amit Gupta, Arkomita Mukherjee, and Asmita Mukherjee, "Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques: A Review”, Advances in Computational Sciences and Technology ISSN 0973-6107 Volume 10, Number 7 (2017), pp. 2137-2159
[13]Miss. Chaitrali S. Dangare, Dr. Mrs. Sulabha S. Apte, “A Data Mining Approach For Prediction Of Heart Disease Using Neural Network”, International Journal of Computer Engineering and Technology (IJCET) Volume 3, Issue 3, October - December (2012), pp. 30-40.
[14]Amandeep Kaur, Jyoti Arora, “Heart Disease Prediction Using Data Mining Techniques: A Survey”, International Journal of Advanced Research in Computer Science, Volume 9, No. 2, March-April 2018, pp. 569-572. DOI: http://dx.doi.org/10.26483/ijarcs.v9i2.5872.
[15]Paria Soleimani, Arezoo Neshati, “Applying the Regression Technique for Prediction of the Acute Heart Attack”, International Journal of Biomedical and Biological Engineering Vol:9, No:11, 2015, pp 767-771.
[16]Xiao Liu, “A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method”, Hindawi Computational and Mathematical Methods in Medicine Volume 2017.DOI: https://doi.org/10.1155/2017/8272091.
[17]Fen Miao, Yun-Peng Cai, Yu-Xiao Zhang, Xiao-Mao, "Predictive Modeling of Hospital Mortality for Patients with Heart Failure by Using an Improved Random Survival Forest”, IEEE Volume 6, 2018, pp. 7244-7253.DOI: 10.1109/ACCESS.2018.2789898
[18]R. Chitra, V. Seenivasagam, "Knowledge Discovery from Heart Disease Dataset Using Optimized Neural Network", Springer International Publishing Switzerland 2013, pp. 423-431.DOI:https://doi.org/10.1007/978-3-319-03844-5_43.
[19]Mohammad A. M. Abushariah, Ali Alqudah, Omar Adwan, “Automatic Heart Disease Diagnosis System Based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Approaches”, Journal of Software Engineering and Applications, 2014, pp. 1055-1064.DOI: 10.4236/jsea.2014.712093.
[20]Sheetal Sonawane, Deepti Vadicherla,” Decision Support System For Heart Disease Based On Sequential Minimal Optimization In Support Vector Machine”, International Journal of Engineering Sciences & Emerging Technologies, Feb. 2013, Volume 4, Issue 2, pp. 19-26.
[21]Nilakshi P. Waghulde, Nilima P. Patil., “Genetic Neural Approach for Heart Disease Prediction”, International Journal of Advanced Computer Research, Volume-4 Number-3 Issue-16, September 2014, pp. 778-783.
[22]Ritika, Chadha, Mayank, Shubhankar, “Prediction of heart disease using data mining techniques”, Springer 2016. DOI: https://doi.org/10.1007/s40012-016-0121-0.
[23]Hidayat TAKCI," Improvement of heart attack prediction by the feature selection methods", Turkish Journal of Electrical Engineering & Computer Sciences 2018, pp. 1-10.DOI:10.3906/elk-1611-235.
[24]Rajesh Jangade, Ritu Chauhan, Ruchita Rekapally,” Classification Model for Prediction of Heart Disease”, Springer Nature Singapore Pte Ltd. 2018, pp. 707-714. DOI: https://doi.org/10.1007/978-981-10-5699-4_67.
[25]E. K. Hashi, M. S. U. Zaman, and M. R. Hasan, "An expert clinical decision support system to predict disease using classification techniques,” 2017 Int. Conf. Electr. Comput. Commun. Eng., pp. 396–400, 2017.
[26]M. Sultana, A. Haider, and M. S. Uddin, "Analysis of data mining techniques for heart disease prediction, "2016 3rd Int. Conf. Electr. Eng. Inf. Commun. Technol. iCEEiCT 2016.
[27]A. Davari Dolatabadi, S. E. Z. Khadem, and B. M. Asl, "Automated diagnosis of coronary artery disease(CAD) patients using optimized SVM," Comput. Methods Programs Biomed., vol. 138, pp. 117–126, 2017. DOI: 10.1155/2018/2016282.
[28]Kaan Uyar, Ahmet İlhan, "Diagnosis of heart disease using a genetic algorithm based trained recurrent fuzzy neural networks”, 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, ICSCCW, Budapest, Hungary, Volume 120, August 2017, pp. 588-593.DOI: https://doi.org/10.1016/j.procs.2017.11.283.
[29]Ashok Kumar Dwivedi, “Performance evaluation of different machine learning techniques for prediction of heart disease”, Springer, The Natural Computing Applications Forum 17 September 2016, Volume 29, Issue 10, pp. 685–693. DOI: https://doi.org/10.1007/s00521-016-2604-1.
[30]R. Kannan, V. Vasanthi, “Machine Learning Algorithms with ROC Curve for Predicting and Diagnosing the Heart Disease”, Springer Briefs in Forensic and Medical Bioinformatics, 14 June 2018, pp 63-72. DOI: https://doi.org/10.1007/978-981-13-0059-2_8.
[31]Sairabi H. Mujawar1, P. R. Devale, "Prediction of Heart Disease using Modified K-means and by using Naive Bayes", International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 3, Issue 10, October 2015, pp. 10265-10273. DOI: 10.15680/IJIRCCE.2015. 0310123.
[32]Amita Malav, Kalyani Kadam, Pooja Kamat, “Prediction Of Heart Disease Using K-Means And Artificial Neural Network as Hybrid Approach to Improve Accuracy”, International Journal of Engineering and Technology (IJET), Vol. 9 August 2017, pp. 3081-3085. DOI: 10.21817/ijet/2017/v9i4/170904101.
[33]Purushottam, Prof. (Dr.) Kanak Saxena, Richa Sharma, “Efficient Heart Disease Prediction System”, 2016, pp.962-969.DOI: https://doi.org/10.1016/j.procs.2016.05.288.
[34]Vikas Chaurasia, Saurabh Pal, “Early Prediction of Heart Diseases Using Data Mining Techniques”, Caribbean Journal of Science and Technology, Vol. 1 December 2013, pp. 208-217.