Feature Ensemble Learning Based on Sparse Autoencoders for Diagnosis of Parkinson’s Disease

Machine learning approaches to identify Parkinson's disease using voice signal features

Parkinson's Disease (PD) is the second most common age-related neurological disorder that leads to a range of motor and cognitive symptoms. A PD diagnosis is difficult since its symptoms are quite similar to those of other disorders, such as normal aging and essential tremor. When people reach 50, visible symptoms such as difficulties walking and communicating begin to emerge. Even though there is no cure for PD, certain medications can relieve some of the symptoms. Patients can maintain their lifestyles by controlling the complications caused by the disease. At this point, it is essential to detect this disease and prevent it from progressing. The diagnosis of the disease has been the subject of much research. In our project, we aim to detect PD using different types of Machine Learning (ML), and Deep Learning (DL) models such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) to differentiate between healthy and PD patients by voice signal features. The dataset taken from the University of California at Irvine (UCI) machine learning repository consisted of 195 voice recordings of examinations carried out on 31 patients. Moreover, our models were trained using different techniques such as Synthetic Minority Over-sampling Technique (SMOTE), Feature Selection, and hyperparameter tuning (GridSearchCV) to enhance their performance. At the end, we found that MLP and SVM with a ratio of 70:30 train/test split using GridSearchCV with SMOTE gave the best results for our project. MLP performed with an overall accuracy of 98.31%, an overall recall of 98%, an overall precision of 100%, and f1-score of 99%. In addition, SVM performed with an overall accuracy of 95%, an overall recall of 96%, an overall precision of 98%, and f1-score of 97%. The experimental results of this research imply that the proposed method can be used to reliably predict PD and can be easily incorporated into healthcare for diagnosis purposes.

Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson's disease

Speech diagnosis of Parkinson’s disease (PD) as a non-invasive and simple diagnosis method is particularly worth exploring. However, the number of samples of speech-based PD is relatively small, and there exist discrepancies in the distribution between subjects. In order to solve the two problems, a novel unsupervised two-step sparse transfer learning is proposed in this paper to tackle with PD speech diagnosis. In the first step, convolution sparse coding with the coordinate selection of samples and features is designed to learn speech structure from the source domain to replenish sample information of the target domain. In the second step, joint local structure distribution alignment is designed to maintain the neighbor relationship between the respective samples of the training set and test set, and reduce the distribution difference between the two domains at the same time. Two representative public PD speech datasets and one real-world PD speech dataset were exploited to verify the proposed method on PD speech diagnosis. Experimental results demonstrate that each step of the proposed method has a positive effect on the PD speech classification results, and it also delivers superior performance over the existing relative methods.

Evaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurements

Parkinson's disease is a neurological disorder that causes partial or complete loss of motor reflexes and speech and affects thinking, behavior, and other vital functions affecting the nervous system. Parkinson's disease causes impaired speech and motor abilities (writing, balance, etc.) in about 90% of patients and is often seen in older people. Some signs (deterioration of vocal cords) in medical voice recordings from Parkinson's patients are used to diagnose this disease. The database used in this study contains biomedical speech voice from 31 people of different age and sex related to this disease. The performance comparison of the machine learning algorithms k-Nearest Neighborhood (k-NN), Random Forest, Naive Bayes, and Support Vector Machine classifiers was performed with the used database. Moreover, the best classifier was determined for the diagnosis of Parkinson's disease. Eleven different training and test data (45 × 55, 50 × 50, 55 × 45, 60 × 40, 65 × 35, 70 × 30, 75 × 25, 80 × 20, 85 × 15, 90 × 10, 95 × 5) were processed separately. The data obtained from these training and tests were compared with statistical measurements. The training results of the k-NN classification algorithm were generally 100% successful. The best test result was obtained from Random Forest classifier with 85.81%. All statistical results and measured values are given in detail in the experimental studies section.Graphical abstract.