Efficient machine learning model to detect early stage Parkinson’s disease

Abstract

Parkinson’s disease (PD) often manifests itself in memory loss and cognitive decline. The decline is inexorable, and damage to the brain’s cortex has already occurred. Numerous studies have shown that by detecting dementia early and beginning treatment, the disease’s course can be slowed, and any further atrophy can be prevented. Brain imaging data, such as from an MRI, is frequently used in the diagnosis of Parkinson’s disease (PD). In recent years, utilizing deep convolutional neural networks has greatly improved Parkinson’s disease diagnosis. However, getting to the level of quality needed for clinical use is still challenging. In this study, we introduce a machine learning-based approach for more accurately diagnosing Parkinson’s disease. This research makes use of information gleaned from single-photon emission computerized tomography (SPECT) scan and positron emission tomography (PET) scans performed on patients with Parkinson’s disease (PD) and healthy controls. The most crucial characteristics of these datasets are isolated with the aid of the Fisher discriminate ratio (FDR) and non-negative matrix factorization (NMF). The K-nearest neighbor, Decision Tree, Support vector machine (SVM), and Deep Convolution neural network (DCCN) classifiers with confidence bounds classify the NMF-transformed data sets with a decreased number of features. The proposed DCCN technique has a classification accuracy of up to 93.7 percent when compared to decision trees, K-Nearest Neighbor (KNN)s, and SVMs. The DCCN is now a reliable approach for classifying SPECT and PET, PD images.