Enhancing Parkinson’s Disease Prediction Using Deep Learning-Based Convolutional Neural Networks

Abstract

Recently significant advances in the field of medical diagnostics can be seen. Nonetheless, diagnosing Parkinson's disease (PD) remains difficult, especially in terms of timeliness and precision. The inability of present technological methods to identify this complicated neurodegenerative disease emphasizes the urgent need for more research. PD is a complicated neurological condition that is becoming more commonplace worldwide. A timely and precise PD diagnosis is critical because it directly determines the quality of patient care and the success of treatment. However, our current diagnostic approaches, which primarily rely on clinical assessments, have substantial limits in terms of sensitivity and specificity. Thus, the need for a more reliable and objective diagnosing procedure is essential. Convolutional neural networks (CNNs) are employed in this study to investigate complex spiral and wave patterns and to overcome this significant challenge. Drawings from both PD patients and their healthy counterparts were included in the large dataset that was used to train the robust CNN model, which is a key component of the research technique. The training and intensive testing of the model on this dataset confirms its capacity to distinguish between PD instances and non-PD cases. The results of this investigation demonstrate how well the CNN model can predict Parkinson's disease (PD) based on spiral and wave patterns, with a 93% classification accuracy. The high sensitivity and specificity of the model are especially impressive because they significantly lower the chances of false positives and false negatives. The discussion that follows offers a considered evaluation of these findings, emphasising the model's potential for early diagnosis, its capacity to support professional judgments, and its crucial role in enhancing patient outcomes. This study highlights the great potential of deep learning methods for disease diagnosis, especially for PD. The effective integration of the CNN model into a React application for real-time prediction and continuous monitoring of patient performance has important implications for telemedicine and remote healthcare management. This breakthrough makes Parkinson's disease treatment more approachable and proactive.