Parkinson’s Disease Detection Using Hybrid LSTM-GRU Deep Learning Model

Abstract

Parkinson’s disease is the second-most common cause of death and disability as well as the most prevalent neurological disorder. In the last 15 years, the number of cases of PD has doubled. The accurate detection of PD in the early stages is one of the most challenging tasks to ensure individuals can continue to live with as little interference as possible. Yet there are not enough trained neurologists around the world to detect Parkinson’s disease in its early stages. Machine learning methods based on Artificial intelligence have acquired a lot of popularity over the past few decades in medical disease detection. However, these methods do not provide an accurate and timely diagnosis. The overall detection accuracy of machine learning-related models is inadequate. This study collected data from 31 male and female patients, including 195 voices. Approximately six recordings were created per patient, with the length of each recording extending from 1 to 36 s. These voices were recorded in a soundproof studio using an Industrial Acoustics Company (IAC) AKG-C420 head-mounted microphone. The data set was collected to investigate the diagnostic significance of speech and voice abnormalities caused by Parkinson’s disease. An imbalanced dataset is the main contributor of model overfitting and generalization errors, and hence one class has the majority of samples and the other class has minority samples. This problem is addressed in this study by utilizing the three sampling techniques. After balancing the datasets, each class has the same number of samples, which has proven valuable in improving the model’s performance and reducing the overfitting problem. Four performance metrics such as accuracy, precision, recall and f1 score are used to evaluate the effectiveness of the proposed hybrid model. Experiments demonstrated that the proposed model achieved 100% accuracy, recall and f1 score using the balanced dataset with the random oversampling technique and 100% precision, 97% recall, 99% AUC score and 91% f1 score with the SMOTE technique.

Comparative Analysis Using Machine Learning Algorithms to Detect Parkinson’s Disease using Voice Dataset

“…By leveraging these algorithms on a dataset comprising healthy and PD images, the accuracy rate has shown a significant improvement of 74%. This approach aids clinicians in more efficiently identifying and treating individuals with PD, potentially leading to better patient outcomes [11]. In this research, a novel approach combining Bag-of-Visual Words (BoVW) and Deep Optimum-Path Forest classifier is proposed for automatic Parkinson's disease identification.…”

Optimizing Cervical Cancer Classification with SVM and Improved Genetic Algorithm on Pap Smear Images

“…The proposed method, IGA-SVM, is compared with existing machine learning algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Naive Bayes. The evaluation metrics used for comparison include Accuracy, Precision, Recall, and F1-Measure [25].…”

Ensemble Machine Learning Approach for Parkinson’s Disease Detection Using Speech Signals

“…Owing to its capacity to handle large datasets, deep learning techniques have also been applied to the effective diagnosis of PD [19]. By utilizing voice signal characteristics and data balancing techniques, a hybrid LSTM-GRU model proposed by [20] achieved a noteworthy accuracy of 100%. Research conducted by [21] proposed a hybrid CNN-LSTM model that works in various stages, including noise removal, feature extraction, and the final classification stage.…”