- Volume 6 Issue 2
DOI QR Code
Comparative Study to Measure the Performance of Commonly Used Machine Learning Algorithms in Diagnosis of Alzheimer's Disease
- kumar, Neeraj (Department of Computer Science & IT, University of Jammu) ;
- manhas, Jatinder (Department of Computer Science & IT, Bhaderwah Campus, University of Jammu) ;
- sharma, Vinod (Department of Computer Science & IT, University of Jammu)
- Received : 2019.04.30
- Accepted : 2019.05.27
- Published : 2019.06.30
In machine learning, the performance of the system depends upon the nature of input data. The efficiency of the system improves when the behavior of the input data changes from un-normalized to normalized form. This paper experimentally demonstrated the performance of KNN, SVM, LDA and NB on Alzheimer's dataset. The dataset undertaken for the study consisted of 3 classes, i.e. Demented, Converted and Non-Demented. Analysis shows that LDA and NB gave an accuracy of 89.83% and 88.19% respectively in both the cases whereas the accuracy of KNN and SVM improved from 46.87% to 82.80% and 53.40% to 88.75% respectively when input data changed from un-normalized to normalized state. From the above results it was observed that KNN and SVM show significant improvement in classification accuracy on normalized data as compared to un-normalized data, whereas LDA and NB reflect no such change in their performance.
Alzheimer's disease;KNN;Machine learning;Neurodegeneration
- G. D. Magoulas and A. Prentza, "Machine Learning in Medical Applications," Machine Learning and Its Applications, ACAI 1999, Lecture Notes in Computer Science, vol. 2049, pp. 300-307, 2001.
- M. Li and Z. Zhou, "Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples," IEEE Transactions on Systems, Man, and Cybernetics - Part A:Systems and Humans, vol. 37, no. 6, pp. 1088-1098, 2007.
- A. Sarwar, V. Sharma, and R. Gupta, "Hybrid ensemble learning technique for screening of cervical cancer using Papanicolaou smear image analysis," Personalized Medicine Universe, vol. 4, pp. 54-62, 2015. https://doi.org/10.1016/j.pmu.2014.10.001
- B. K. Singh, K. Verma, and A. S. Thoke, "Investigations on Impact of Feature Normalization Techniques on Classifier's Performance in Breast Tumor Classification," International Journal of Computer Applications, vol. 116, issue 19, pp. 11-15, 2015.
- G. Fung and J. Stoeckel, "SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information," Knowledge and Information Systems, vol. 11, issue 2, pp. 243-258, 2007. https://doi.org/10.1007/s10115-006-0043-5
- J. M. Gorriz, J. Ramirez, A. Lassl, D. Gonzalez, E. W. Lang, C. G. Puntonet, I. Alvarez, M. Lopez, and M. G. Rio, "Automatic computer aided diagnosis tool using component-based SVM," in 2008 IEEE Nuclear Science Symposium Conference Record, Dresden, Germany, pp. 4392-4395, 2008.
- J. F. Horn, M. O. Habert, A. Kas, Z. Malek, P. Maksud, L. Lacomblez, A. Giron, and B. Fertil, "Differential automatic diagnosis between Alzheimer's disease and frontotemporal dementia based on perfusion SPECT images," Artificial Intelligence in Medicine, vol. 47, issue 2, pp. 147- 158, 2009. https://doi.org/10.1016/j.artmed.2009.05.001
- M. M. Lopez, J. Ramirez, J. M. Gorriz, I. A lvarez, D. S. Gonzalez, F. Segovia, and R. Chaves, "SVM-based CAD system for early detection of the Alzheimer's disease using kernel PCA and LDA," Neuroscience Letters, vol. 464, pp. 233-238, 2009. https://doi.org/10.1016/j.neulet.2009.08.061
- L. Huang, Z. Pan, H. Lu, and ADNI, "Automated Diagnosis of Alzheimer's Disease with Degenerate SVM-Based Adaboost," in 2013 5th International Conference on Intelligent Human- Machine Systems and Cybernetics, Hangzhou, pp. 298-301, 2013.
- S. Alam, G. R. Kwon, and ADNI, "Alzheimer disease classification using KPCA, LDA and multi-kernel learning SVM," in International Journal of Imaging Systems and Technology, vol. 27, pp. 133-143, 2017. https://doi.org/10.1002/ima.22217
- D. Cai, X. He, and J. Han, "Training Linear Discriminant Analysis in Linear Time," IEEE 24th International Conference on Data Engineering, Cancun, 2008, pp. 209-217.
- K. Larsen, "Generalized Naïve Bayes Classifiers," ACM SIGKDD Explorations Newsletter - Natural language processing and text mining, vol. 7, issue 1, pp. 76-81, 2005.
- L. B. Moreira and A. A. Namen, "A hybrid data mining model for diagnosis of patients with clinical suspicion of dementia," Computer Methods and Programs in Biomedicine, vol. 165, pp. 139-149, 2018. https://doi.org/10.1016/j.cmpb.2018.08.016
- W. Cherif, "Optimization of K-NN algorithm by clustering and reliability coefficients: application to breast-cancer diagnosis," Procedia Computer Science, vol. 127, issue C, pp. 293-299, 2018. https://doi.org/10.1016/j.procs.2018.01.125
- A. Suresh, R. Kumar, and R. Varatharajan, "Health care data analysis using evolutionary algorithm," The Journal of Supercomputing, pp. 1-10, 2018.
- P. Samant and R. Agarwal, "Machine learning techniques for medical diagnosis of diabetes using iris images," Computer Methods and Programs in Biomedicine, vol. 157, pp. 121-128, 2018. https://doi.org/10.1016/j.cmpb.2018.01.004