| Autor: |
Hathaliya J; Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India., Modi H; Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India., Gupta R; Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India., Tanwar S; Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India., Alqahtani F; Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia., Elghatwary M; Biomedical Technology Department, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi Arabia., Neagu BC; Power Engineering Department, Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania., Raboaca MS; National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Rm. Valcea, Uz-inei Street, No. 4, P.O. Box 7 Raureni, 240050 Râmnicu Vâlcea, Romania. |
| Abstrakt: |
Parkinson's disease (PSD) is a neurological disorder of the brain where nigrostriatal integrity functions lead to motor and non-motor-based symptoms. Doctors can assess the patient based on the patient's history and symptoms; however, the symptoms are similar in various neurodegenerative diseases, such as progressive supranuclear palsy (PSP), multiple system atrophy-parkinsonian type (MSA), essential tremor, and Parkinson's tremor. Thus, sometimes it is difficult to identify a patient's disease based on his or her symptoms. To address the issue, we have used neuroimaging biomarkers to analyze dopamine deficiency in the brains of subjects. We generated the different patterns of dopamine levels inside the brain, which identified the severity of the disease and helped us to measure the disease progression of the patients. For the classification of the subjects, we used machine learning (ML) algorithms for a multivariate classification of the subjects using neuroimaging biomarkers data. In this paper, we propose a stacked machine learning (ML)-based classification model to identify the HC and PSD subjects. In this stacked model, meta learners can learn and combine the predictions from various ML algorithms, such as K-nearest neighbor (KNN), random forest algorithm (RFA), and Gaussian naive Bayes (GANB) to achieve a high performance model. The proposed model showed 92.5% accuracy, outperforming traditional schemes. |
