The Impact of Feature Selection Techniques on the Performance of Predicting Parkinson’s Disease

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Abdullah Al Imran 1,* Ananya Rahman 2 Md Humayoun Kabir 3 Md Shamsur Rahim 1

1. American International University-Bangladesh, Dhaka, Bangladesh

2. Kumudini Women's Medical College (KWMC), Mirzapur, Tangail, Bangladesh

3. Community Based Medical College, Bangladesh (CBMCB), Mymensingh, Bangladesh

* Corresponding author.


Received: 1 May 2018 / Revised: 5 Jul. 2018 / Accepted: 12 Aug. 2018 / Published: 8 Nov. 2018

Index Terms

Parkinson’s Disease, Feature selection, Feature ranking technique, Classification, Data Mining, Accuracy, Sensitivity


Parkinson’s Disease (PD) is one of the leading causes of death around the world. However, there is no cure for this disease yet; only treatments after early diagnosis may help to relieve the symptoms. This study aims to analyze the impact of feature selection techniques on the performance of diagnosing PD by incorporating different data mining techniques. To accomplish this task, identifying the best feature selection approach was the primary focus. In this paper, the authors had applied five feature selection techniques namely: Gain Ratio, Kruskal-Wallis Test, Random Forest Variable Importance, RELIEF and Symmetrical Uncertainty along with four classification algorithms (K-Nearest Neighbor, Logistic Regression, Random forest, and Support Vector machine) on the PD dataset collected from the UCI Machine Learning repository. The result of this study was obtained by taking the four different subsets (Top 5, 10, 15, and 20 features) from each feature selection approach and applying the classifiers. The obtained result showed that in terms of accuracy, Random Forest Variable Importance, Gain Ratio, and Kruskal-Wallis Test techniques generated the highest 89% score. On the other hand, in terms of sensitivity, Gain Ratio and Kruskal-Walis Test approaches produced the highest 97% score. The findings of this research clearly indicated the impact of feature selection techniques on predicting PD and our applied methods outperformed the state-of-the-art performance.

Cite This Paper

Abdullah Al Imran, Ananya Rahman, Humayoun Kabir, Shamsur Rahim, "The Impact of Feature Selection Techniques on the Performance of Predicting Parkinson’s Disease", International Journal of Information Technology and Computer Science(IJITCS), Vol.10, No.11, pp.14-29, 2018. DOI:10.5815/ijitcs.2018.11.02


[1], Last accessed at 9:00 PM on 29th March, 2018
[2], Last accessed at 11:00 AM on 3rd April, 2018
[3], Last accessed at 10:00 PM on 9th April, 2018
[4]Little, Max A., et al. "Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection." BioMedical Engineering OnLine 6.1 (2007): 23.
[5]Tsanas, Athanasios, et al. "Accurate tele monitoring of Parkinson's disease progression by noninvasive speech tests." IEEE transactions on Biomedical Engineering 57.4 (2010): 884-893.
[6]Tuite, Paul. "Brain Magnetic Resonance Imaging (MRI) as a Potential Biomarker for Parkinson’s Disease (PD)." Brain sciences 7.6 (2017): 68.
[7]Mostafa, Salama A., et al. "Evaluating the Performance of Three Classification Methods in Diagnosis of Parkinson’s Disease." International Conference on Soft Computing and Data Mining. Springer, Cham, 2018.
[8]Lahmiri, Salim, Debra Ann Dawson, and Amir Shmuel. "Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures." Biomedical Engineering Letters 8.1 (2018): 29-39.
[9]Ramani, R. Geetha, and G. Sivagami. "Parkinson disease classification using data mining algorithms." International journal of computer applications 32.9 (2011): 17-22.
[10]Das, Resul. "A comparison of multiple classification methods for diagnosis of Parkinson disease." Expert Systems with Applications 37.2 (2010): 1568-1572.
[11]Sriram, Tarigoppula VS, et al. "A Comparison And Prediction Analysis For The Diagnosis Of Parkinson Disease Using Data Mining Techniques On Voice Datasets." International Journal of Applied Engineering Research 11.9 (2016): 6355-6360.
[12]Tiwari, Arvind Kumar. "Machine learning based approaches for prediction of Parkinson disease." Mach Learn Appl 3.2 (2016): 33-39.
[13]Srinivasan, Satish M., Michael Martin, and Abhishek Tripathi. "ANN based Data Mining Analysis of the Parkinson’s Disease." International Journal of Computer Applications 168.1 (2017).
[14]Ene, Marius. "Neural network-based approach to discriminate healthy people from those with Parkinson's disease." Annals of the University of Craiova-Mathematics and Computer Science Series 35 (2008): 112-116.
[15]Gil, David, and Devadoss Johnson Manuel. "Diagnosing parkinson by using artificial neural networks and support vector machines." Global Journal of Computer Science and Technology 9.4 (2009).
[16]Rustempasic, Indira, and Mehmet Can. "Diagnosis of parkinson’s disease using fuzzy c-means clustering and pattern recognition." Southeast Europe Journal of Soft Computing 2.1 (2013).
[17]Little, Max A., et al. "Suitability of dysphonia measurements for tele monitoring of Parkinson's disease." IEEE transactions on biomedical engineering56.4 (2009): 1015-1022.
[18]Team, R. Core. "R: A language and environment for statistical computing." (2013).
[19]Bischl, Bernd, et al. "mlr: Machine Learning in R." Journal of Machine Learning Research 17.170 (2016): 1-5.
[20]Quinlan, J. Ross. "Improved use of continuous attributes in C4. 5." Journal of artificial intelligence research 4 (1996): 77-90.
[21]McKight, Patrick E., and Julius Najab. "Kruskal‐Wallis Test." Corsini encyclopedia of psychology (2010).
[22]Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32.
[23]Kira, Kenji, and Larry A. Rendell. "The feature selection problem: Traditional methods and a new algorithm." Aaai. Vol. 2. 1992.
[24]Kira, Kenji, and Larry A. Rendell. "A practical approach to feature selection." Machine Learning Proceedings 1992. 1992. 249-256.
[25]Witten, Ian H., et al. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.
[26]Chawla, Nitesh V., et al. "SMOTE: synthetic minority over-sampling technique." Journal of artificial intelligence research 16 (2002): 321-357.
[27]Larose, Daniel T. "k‐nearest neighbor algorithm." Discovering knowledge in data: An introduction to data mining (2005): 90-106.
[28]Franklin, James. "The elements of statistical learning: data mining, inference and prediction." The Mathematical Intelligencer 27.2 (2005): 83-85.
[29]Cristianini, Nello, and John Shawe-Taylor. An introduction to support vector machines and other kernel-based learning methods. Cambridge university press, 2000.
[30]N. Shamli, B. Sathiyabhama,"Parkinson's Brain Disease Prediction Using Big Data Analytics", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.6, pp.73-84, 2016. DOI: 10.5815/ijitcs.2016.06.10