International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 14, No. 6, 8 Dec. 2022
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Comparative Analysis of Data Mining Techniques to Predict Cardiovascular Disease
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Author(s)
Md. Al Muzahid Nayim
1,*
Fahmidul Alam
1
Md. Rasel
1
Ragib Shahriar
1
Dip Nandi
2
Index Terms
Data Mining, WEKA, Classification Techniques, Cardiovascular Disease (CVD)
Abstract
Cardiovascular disease is the leading cause of death. In recent days, most people are living with cardiovascular disease because of their unhealthy lifestyle and the most alarming issue is the majority of them do not get any symptoms in the early stage. This is why this disease is becoming more deadly. However, medical science has a large amount of data regarding cardiovascular disease, so this data can be used to apply data mining techniques to predict cardiovascular disease at the early stage to reduce its deadly effect. Here, five data mining classification techniques, such as: Naïve Bayes, K-Nearest Neighbors, Support Vector Machine, Random Forest and Decision Tree were implemented in the WEKA tool to get the best accuracy rate and a dataset of 12 attributes with more than 300 instances was used to apply all the data mining techniques to get the best accuracy rate. After doing this research people who are at the early stage of cardiovascular disease or probably going to be a victim can be identified more accurately.
Cite This Paper
Md. Al Muzahid Nayim, Fahmidul Alam, Md. Rasel, Ragib Shahriar, Dip Nandi, "Comparative Analysis of Data Mining Techniques to Predict Cardiovascular Disease", International Journal of Information Technology and Computer Science(IJITCS), Vol.14, No.6, pp.23-32, 2022. DOI:10.5815/ijitcs.2022.06.03
Reference
[1]Manisha Barman, J Paul Chaudhury, “A Framework for Selection of Membership Function Using Fuzzy Rule Base System for the Diagnosis of Heart Disease,” Information Technology and Computer Science, vol. 5, num. 11, pp. 62-70, 2013.
[2]Isra’a Ahmed Zriqat, Ahmad Mousa Altamimi, Mohammad Azzeh, “A Comparative Study for Predicting Heart Diseases Using Data Mining Classification Methods,” International Journal of Computer Science and Information Security (IJCSIS), vol. 14(12), pp. 868-879, 2016.
[3]Jyoti Soni, Ujma Ansari, Dipesh Sharma, Sunita Soni, “Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction,” International Journal of Computer Applications (0975 –8887) vol. 17–No.8, March 2011.
[4]Shafenoor Amin, Mohammad; Kia Chiam, Yin; Dewi Varathan, Kasturi. “Identification of Significant Features and Data Mining Techniques in Predicting Heart Disease.” Telematics and Informatics, Vol. 36, pp. 82-93, March 2019.
[5]Pratiksha Shetgaonkar, Dr. Shailendra Aswale, “Heart Disease Prediction using Data Mining Techniques,” International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181 IJERTV10IS020083, vol. 10 Issue 02, February-2021.
[6]David S. Celermajer, Clara K. Chow, Eloi Marijon, Nicholas M. Anstey, Kam S. Woo, “Cardiovascular Disease in the Developing World: Prevalences, Patterns, and the Potential of Early Disease Detection,” Journal of the American College of Cardiology, vol. 60, pp. 1207-1216, 2012.
[7]Vikas Chaurasia, Saurabh Pal, “Data Mining Approach to Detect Heart Diseases,” International Journal of Advanced Computer Science and Information Technology (IJACSIT), vol. 2, no. 4, pp. 56-66, 2013.
[8]Shilpa N. Bhupathiraju; Katherine L. Tucker, “Coronary heart disease prevention: Nutrients, foods, and dietary patterns,” Clinica Chimica Acta, vol. 412, pp 1493-1514, 2011.
[9]J. Thomas, Theresa Princy. R, “Human Heart Disease Prediction System Using Data Mining Techniques,” IEEE 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT) - Nagercoil, India, 2016.
[10]Obanijesu Opeyemi, Emuoyibofarhe O. Justice, “Development of Neuro-fuzzy System for Early Prediction of Heart Attack,” Information Technology and Computer Science, vol. 4, num. 9, pp. 22-28, 2012.
[11]M. Vijayavanan, V. Rathikarani, Dr. P. Dhanalakshmi, “Automatic Classification of ECG Signal for Heart Disease Diagnosis using Morphological Features,” International Journal of Computer Science & Engineering Technology (IJCSET),” ISSN: 2229-3345, vol. 5, No. 04, pp. 449-455, 2014.
[12]Borejda Xhyheri; Raffaele Bugiardini, “Diagnosis and Treatment of Heart Disease: Are Women Different from Men?” Progress in Cardiovascular Diseases, vol. 53, pp. 227–236.2010.
[13]Serkan Kiranyaz, Turker Ince, Jenni Pulkkinen, Moncef Gabbouj, “Personalized Long-term ECG Classification: A Systematic Approach,” Expert Systems with Applications, vol. 38, pp. 3220–3226, 2011.
[14]Fahim Sufi, Ibrahim Khalil, “Diagnosis of Cardiovascular Abnormalities from Compressed ECG: A Data Mining-Based Approach,” IEEE Transactions on Information Technology in Biomedicine, vol. 15, pp. 33-39, 2011.
[15]Dimitri Grün, Felix Rudolph, Nils Gumpfer, Jennifer Hannig, Laura K. Elsner, Beatrice von Jeinsen, Christian W. Hamm, Andreas Rieth3, Michael Guckert, Till Keller, “Identifying Heart Failure in ECG Data with Artificial Intelligence—A Meta-Analysis,” Frontiers in Digital Health, February 2021 | Volume 2 | Article 584555.
[16]J. K. Oh, “Echocardiography in heart failure: Beyond diagnosis,” European Journal of Echocardiography, vol. 8, pp. 4-14, 2007.
[17]Vasiliki V. Georgiopoulou MD, Andreas P. Kalogeropoulos MD, Paolo Raggi MD, Javed Butler MD, MPH, “Prevention, Diagnosis, and Treatment of Hypertensive Heart Disease,” Cardiology Clinics, vol. 28, pp. 675– 691, 2010.
[18]John D. Groarke1, Paul L. Nguyen, Anju Nohria, Roberto Ferrari, Susan Cheng and Javid Moslehi, “Cardiovascular complications of radiation therapy for thoracic malignancies: The Role for Non-invasive Imaging for Detection of Cardiovascular Disease,” European Heart Journal, vol. 35, pp. 612–623, 2014.
[19]Morteza Tavakol MD, Salman Ashraf MD & Sorin J. Brener MD, “Risks and Complications of Coronary Angiography: A Comprehensive Review,” Global Journal of Health Science, vol. 4, No. 1, pp. 65-93, 2012.
[20]Sushil A. Luis, Patricia A. Pellikka, “Best Practice & Research Clinical Endocrinology & Metabolism,” Division of Cardiovascular Diseases, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
[21]Jorge A. Gonzalez, Christopher M. Kramer, “Role of Imaging Techniques for Diagnosis, Prognosis and Management of Heart Failure Patients: Cardiac Magnetic Resonance,” Springer Science+Business Media (Springer), vol. 12(4), pp. 276-283, 2015 doi: 10.1007/s11897-015-0261-9
[22]MD Peter J. Cawley, MD Jeffrey H. Maki, MD Catherine M. Otto, “Cardiovascular Magnetic Resonance Imaging for Valvular Heart Disease: Technique and Validation,” Circulation, vol. 119(3), pp. 468-478, doi: 10.1161/CIRCULATIONAHA.107.742486
[23]Aqueel Ahmed, Shaikh Abdul Hannan, “Data Mining Techniques to Find Out Heart Diseases: An Overview,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-1, 2012.
[24]Bhatla, Nidhi; Jyoti, Kiran, “An Analysis of Heart Disease Prediction using Different Data Mining Techniques,” International Journal of Engineering Research & Technology (IJERT), vol. 1, ISSN: 2278-0181 ,2012.
[25]Mudasir M Kirmani, “Cardiovascular Disease Prediction Using Data Mining Techniques: A Review,” Oriental Journal of Computer Science & Technology, vol. 10, pp. 520-528, 2017.
[26]K. Srinivas, G. Raghavendra Rao, A. Govardhan, “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), pp. 1344 - 1349, 2010.
[27]Senthilkumar Mohan, Chandrasegar Thirumalai, Gautam Srivastava, “Effective Heart Disease Prediction using Hybrid Machine Learning Techniques,” IEEE Access, vol. 7, pp. 81542-81554, 2019.
[28]Milan Kumari, Sunila Godara, “Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction 1,” IJCST, vol. 2, ISSN: 2229- 4333(Print) | ISSN: 0976-8491(Online), 2011.
[29]Dhanashree S. Medhekar, Mayur P. Bote, Shruti D. Deshmukh, “Heart Disease Prediction System using Naive Bayes,” International Journal of Enhanced Research in Science Technology & Engineering, vol. 2, ISSN NO: 2319-7463, 2013.
[30]Mrs. G. Subbalakshmi, Mr. K. Ramesh, Mr. M. Chinna Rao, “Decision Support in Heart Disease Prediction System using Naive Bayes,” Indian Journal of Computer Science and Engineering (IJCSE), vol. 2, pp. 170-176, 2011.
[31]Shadab Adam Pattekari and Asma Parveen, “Prediction System for Heart Disease Using Naive Bayes,” International Journal of Advanced Computer and Mathematical Sciences, vol. 3, pp. 290-294, 2012.
[32]Umair Shafique, Fiaz Majeed, Haseeb Qaiser, and Irfan Ul Mustafa, “Data Mining in Healthcare for Heart Diseases,” International Journal of Innovation and Applied Studies, vol. 10, pp. 1312-1322, 2015.
[33]Aditya Methaila, Prince Kansal, Himanshu Arya, Pankaj Kumar, “Early Heart Disease Prediction Using Data Mining Techniques,” CCSEIT, DMDB, ICBB, MoWiN, AIAP – 2014, pp. 53–59, 2014.
[34]Fabio Mendoza Palechor*, Alexis De la Hoz Manotas, Paola Ariza Colpas, Jorge Sepulveda Ojeda, Roberto Morales Ortega, Marlon Piñeres Melo, “Cardiovascular Disease Analysis Using Supervised and Unsupervised Data Mining Techniques,” Journal of Software, vol. 12, 2017.
[35]Wan Hajarul Asikin Wan Zunaidi, RD Rohmat Saedudin, Zuraini Ali Shah, Shahreen Kasim,Choon Sen Seah and Maman Abdurohman, “Performances Analysis of Heart Disease Dataset using Different Data Mining Classifications,” International Journal on Advanced Science Engineering and Information Technology, vol. 8, pp. 2677-2682, 2018.
[36]M.Akhil jabbar, B.L Deekshatulu, Priti Chandra, “Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm,” Procedia Technology, vol. 10, pp. 85-94, 2013.
[37]Indu Yekkala, Sunanda Dixit, “Prediction of Heart Disease Using Random Forest and Rough Set Based Feature Selection,” International Journal of Big Data and Analytics in Healthcare, vol. 3, pp. 1-12, 2018.
[38]M. A. Jabbar, B. L. Deekshatulu and Priti Chandra, “Intelligent Heart Disease Prediction System using Random Forest and Evolutionary Approach,” Journal of Network and Innovative Computing, vol. 4, pp. 175-184, 2016.