Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
SUMMARY: The purpose of this project is to construct prediction model using various machine learning algorithms and to document the end-to-end steps using a template. The Parkinson’s Disease dataset is a binary classification situation where we are trying to predict one of the two possible outcomes.
INTRODUCTION: The data used in this study were gathered from 188 patients with PD (107 men and 81 women) with ages ranging from 33 to 87 at the Department of Neurology in Cerrahpasa Faculty of Medicine, Istanbul University. The control group consists of 64 healthy individuals (23 men and 41 women) with ages varying between 41 and 82. During the data collection process, the microphone is set to 44.1 KHz and following the physician’s examination, the sustained phonation of the vowel /a/ was collected from each subject with three repetitions.
ANALYSIS: The baseline performance of the machine learning algorithms achieved an average accuracy of 81.58%. Two algorithms (Extra Trees and Stochastic Gradient Boosting) achieved the top accuracy metrics after the first round of modeling. After a series of tuning trials, Extra Trees turned in the top overall result and achieved an accuracy metric of 88.09%. By using the optimized parameters, the Extra Trees algorithm processed the testing dataset with an accuracy of 87.22%, which was just slightly below the prediction accuracy using the training data.
CONCLUSION: For this iteration, the Extra Trees algorithm achieved the best overall results using the training and testing datasets. For this dataset, Extra Trees should be considered for further modeling or production use.
Dataset Used: Parkinson’s Disease Classification Data Set
Dataset ML Model: Binary classification with numerical attributes
Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Parkinson%27s+Disease+Classification
Sakar, C.O., Serbes, G., Gunduz, A., Tunc, H.C., Nizam, H., Sakar, B.E., Tutuncu, M., Aydin, T., Isenkul, M.E. and Apaydin, H., 2018. A comparative analysis of speech signal processing algorithms for Parkinsonâ€™s disease classification and the use of the tunable Q-factor wavelet transform. Applied Soft Computing, DOI: https://doi.org/10.1016/j.asoc.2018.10.022
The HTML formatted report can be found here on GitHub.