Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide. Early detection of PD is crucial for effective treatment and management of the disease. Deep learning (DL) and machine learning (ML) have emerged as promising approaches for detecting PD. In this study, a comparative performance analysis is done for DL and ML applications based on speech signals. DL methods using convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and ML methods employing random forest and the XGBoost model were trained and assessed. Performance of the models are evaluated using a variety of performance metrics, including accuracy, precision, recall, and F1-score. Results showed that the XGBoost model outperformed the DL models in terms of accuracy and F1 score, while the CNN and LSTM models achieved higher precision and recall. These findings suggest that XGBoost can be a useful tool for detecting PD based on speech signals, particularly in scenarios where interpretability and computational efficiency are important.
TopThere have been various studies conducted to deal with the early detection of Parkinson’s disease using XGBoost, Deep Learning and various machine learn- ing models. These work demonstrate the effectiveness of XGBoost in detect- ing Parkinson’s disease at an early stage using voice samples. The results show that XGBoost can achieve high accuracy, sensitivity, and specificity in detecting Parkinson’s disease, which can potentially improve early diagnosis and treatment of the disease. Parkinson’s disease can be detected using deep neural networks such as CNN (Johri et al., 2019). However, as shown in our finding it is observed that the accuracy of Parkinson’s’ disease detection using CNN has low precision. In neurological diseases such as dementia, Alzheimer and Parkinson, the dis- ease can be detected based on feature speech signals therefore by performing
Key Terms in this Chapter
Deep Learning: Deep learning is an extension of machine learning that makes use of artificial neural networks to simulate how the human brain learns.
Long Short-Term Memory: Information can last thanks to a deep learning, sequential neural network. By default, LSTM can save the data for a very long time. It is utilized for time-series data processing, forecasting, and classification.
XGBoost: It is a distributed, scalable gradient-boosted decision tree (GBDT) machine learning framework. It offers parallel tree boosting and is the top ma- chine learning package for issues including regression, classification, and ranking.
Convolutional Neural Networks: It is a deep learning network design that derives its knowledge directly from data. CNNs are very helpful for recognizing objects, classifications, and categories in photos by looking for patterns in the images.
Machine Learning: It is a subfield of artificial intelligence (AI) that focuses on using data and algorithms to mimic human learning processes and progressively increase accuracy.
Parkinson’s Disease: This disease affects the neurological system and the areas of the body that are under the control of the nerves. It is a progressive sickness. It results in unintentional or uncontrollable motions including shaking, stiffness, and trouble balancing, and coordinating one’s movements.