Detection of Parkinson's Disease by Employing Boosting Algorithms

Parkinson's Disease is caused by a decline in the production of dopamine due to the degeneration of brain cells. Dopamine is responsible for the communication between parts of the brain associated with the control and fluency of body movements. Hence, the disease manifests with a spectrum of movement disorders as well as non-motor features. It is now revealed that the non-motor symptoms may show many years prior to the onset of motor symptoms. Therefore, early and accurate diagnosis is crucial to stop or slow down the progression of the disease in its tracks. In this context, ensemble machine learning (ML) algorithms like boosting algorithms can play a significant role in detecting Parkinson's Disease at an early stage. In this paper, four boosting algorithms are studied and implemented in UCI Parkinson's Disease dataset. After rigorous simulation, the ML models exhibited satisfactory results in terms of different performance parameters like accuracy, precision, recall, F1-Score., AUC, Youden, specificity and error rate. However, the performances of the model are improved by tuning the hyperparameters with GridSearchCV. Hence, a detailed comparative analysis is portrayed where Light GBM displayed the highest accuracy of 93.39% after hyperparameter tuning. However, XGBoost and Gradient Boosting algorithm also depicted accuracies more than 90% but AdaBoost demonstrated maximum 87.22% accuracy with hyperparameter tuning.