Deep Learning and Machine Learning for Timely Detection of Parkinson's Disease

Abstract

Parkinson’s disease (PD) is a degenerative neurological disorder with a significant societal impact, which demonstrates the need for early-stage diagnosis and treatment. This research investigates the potential of Deep Learning and Machine Learning methods to support timely PD diagnosis. The proposed model leverages multiple data types such as medical imaging, sensor data, and clinical assessments to detect subtle patterns that indicate PD’s early stages. The dataset was collected to investigate the diagnostic value of speech and voice disturbances caused by PD. Class imbalance is the chief fault of the model overfitting and generalization errors. It is the disparity between one class, which includes all the most samples, and the other class, which includes all the least samples. This research addresses this flaw by employing three sample strategies. By aligning the number of samples in each class via dataset balancing, the classifier’s performance is improved and the problem of overfitting is minimized. The proposed hybrid model demonstrated effectiveness through a myriad of metrics used in evaluating its precision, accuracy, recall, and f1 score. Testing with a balanced dataset utilizing random oversampling revealed the simulation performed with optimal accuracy, recall, and f1 score. When employing the SMOTE technique, it generated 100% precision, a remarkable 97% recall, an outstanding AUC score of 99%, and a strong 91% f1 score. In brief, early returns signify a promising outlook, validating the competency of deep and machine learning methods in bettering the diagnosis of Parkinson's disease–thus empowering earlier treatment choices. Further validation involving more substantial datasets and clinical studies will be required to ascertain the feasibility and viability of the proposed approach. Larger-scale assessment may also provide deeper insight into maximizing performance metrics and optimizing for real-world diagnostic assistance.