Advanced Parkinson’s Disease Detection: A comprehensive artificial intelligence approach utilizing clinical assessment and neuroimaging samples

Abstract

Medical experts are utilizing neuroimaging and clinical assessments to enhance the early identification of Parkinson’s disease. The current research initiative offers ways to identify Parkinson’s disease using machine learning and transfer learning. To carry out this, we extracted 7500 MRI images from 2022 and 2023 and 12 clinical assessment records from 2010 to 2023 from the well-known Parkinson’s Progression Marker Initiative (PPMI) database. Then, we applied machine and transfer learning approaches using clinical assessment records and MRI images, respectively. To identify Parkinson’s Disease (PD) using samples from clinical assessments, four distinct resampling techniques were employed. Subsequently, three machine learning models were applied to train on these resample records, and the recall score was analyzed. A hybrid of SMOTE and ENN proved to be the most effective approach for handling all of the imbalanced data, according to the recall study. Later, four different feature selection methods were used to find the top 10 features using these new samples. Lastly, we trained and validated the model using nine machine-learning algorithms. We also used explainable AI techniques like LIME and SHAP to interpret clinical assessment records. The extra tree classifier outperformed the others in terms of accuracy, reaching 98.44% using the tree-based feature selection technique. In addition to examining clinical assessment samples, this study investigated Parkinson’s disease using neuroimaging data. In pursuit of this objective, four pre-trained architectures were employed to analyze MRI images through two distinct approaches. The first approach involved utilizing the convolutional layer while replacing the remaining two layers with a customized Artificial Neural Network (ANN). Subsequently, training and evaluation are performed using our MRI samples, followed by analyzing significant weights using a LIME interpretable explainer. The second approach employs an improvisational technique without directly replacing the last layer. Instead, we predicted the weights of our MRI samples using the knowledge of the pre-trained model and stored them. Finally, CNN architecture was utilized for Parkinson’s disease detection, achieving an optimal accuracy of 85.08% with the implementation of DenseNet169 and CNN.

Keywords

• Machine learning
• Explainable artificial intelligence
• Local interpretable model agnostic explanations
• Shapley additive explanations