Comparative Analysis of Multiple classifiers for Heart Disease Classification
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Abstract
Over the last decade heart disease remains the main reason for death in the world wide. Several data mining techniques and analysis have been used by the researchers to help health care professionals in the diagnosis of heart disease but using the old traditional techniques can reduce the number of test that is required. With the vast growing death rate in heart disease worldwide it is sure that there must be a quick and efficient detection technique. Supervised machine learning algorithm is one of the effective data analysis methods used. This kind of research compares with different algorithms such asLogistic regression (LR), artificial neural network (ANN), K- Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF) classification seeking better performance in heart disease diagnosis. The dataset (Framingham) consists of 23138 instances and 16 attributes. Subsequently, the classification algorithm which has optimal potential will be suggested for use of sizeable data. The maximum accuracy achieved is 100% train part (60%), test part (40%) by Framingham classifier.
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References
P. Groves, B. Kayyali, D. Knott, and S. V. Kuiken, “The’big data’revolution in healthcare: Accelerating value and innovation,†2016
M. Chen, S. Mao, and Y. Liu, “Big data: A survey,†Mobile Networks and Applications, vol. 19, pp. 171–209, 2014
J. Wang, M. Qiu, and B. Guo, “Enabling real-time information service on telehealth system over cloud-based big data platform,†Journal of Systems Architecture, vol. 72, pp. 69–79, 2017.
D. W. Bates, S. Saria, L. Ohno-Machado, A. Shah, and G. Escobar, “Big data in health care: using analytics to identify and manage high-risk and high-cost patients,†Health Affairs, vol. 33, no. 7, pp. 1123–1131, 2014
Helma, C., E. Gottmann, and S. Kramer, “Knowledge discovery and data mining in toxicology,†Statistical Methods in Medical Research, 2000.
Dhomse Kanchan B and Mahale Kishor M. et al. “Study of Machine Learning Algorithms for Special Disease Prediction using Principal of Component Analysisâ€, International Conference on Global Trends in Signal Processing, Information Computing and Communication, 2016.
Akram Pasha and P H. Latha; “Bioinspired dimensionality reduction for Parkinson’s disease (PD) classification†Health Information Science and System, 2020.
Monika Gandhi , Shailendra Narayanan Singh; ‘Predictions in heart disease using techniques of data mining’ International Conference Futuristic trends on Computational analysis and Knowledge Management, 2015.
M. Nikhil Kumar, K. V. S. Koushik, K. Deepak; “Heart diseases using data mining and machine learning algorithms and tools†International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3, 2456-3307, 2020.
Mai Shouman, Tim Turner, Rob Stocker, “Applying k-Nearest Neighbour in Diagnosing Heart Disease Patients,†International Journal of Information and Education Technology, 2 (3), 220-223, 2012.
Min Chen, Yixue Hao, Kai Hwang, Fellow, IEEE, Lu Wang, and Lin Wang; “Disease Prediction by Machine Learning over Big Data from Healthcare Communities,†IEEE access, 2017.
Shan Xu, Tiangang Zhu, Zhen Zang, Daoxian Wang, Junfeng Hu and Xiaohui Duan et al. “Cardiovascular Risk Prediction Method Based on CFS Subset Evaluation and Random Forest Classification Frameworkâ€, IEEE 2nd International Conference on Big Data Analysis, 2017.
Mitali S Mhatre, Dr.Fauzia Siddiqui, Mugdha Dongre, Paramjit Thakur; “A Review paper on Artificial Neural Network: A Prediction Technique,†International Journal of Scientific & Engineering Research, Volume 6, Issue 12, December-2015.
Zhang, G.; Patuwo, B.E.; Hu, M.Y. “Forecasting with artificial neural networks: The state of the art†International Journal Forecast, 14, 35–62, 1998.
Longjian Liu; “Chapter 4-Biostatistical basis of inference in heart failure study,†heart failure: epidemiology and research methods, pp. 43-82, 2018.