Hybrid feature selection framework for the parkinson imbalanced dataset prediction problem
Yükleniyor...
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
MDPI
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Background and Objectives: Recently, many studies have focused on the early detection of
Parkinson’s disease (PD). This disease belongs to a group of neurological problems that immediately
affect brain cells and influence the movement, hearing, and various cognitive functions. Medical
data sets are often not equally distributed in their classes and this gives a bias in the classification
of patients. We performed a Hybrid feature selection framework that can deal with imbalanced
datasets like PD. Use the SOMTE algorithm to deal with unbalanced datasets. Removing the
contradiction from the features in the dataset and decrease the processing time by using Recursive
Feature Elimination (RFE), and Principle Component Analysis (PCA). Materials and Methods: PD
acoustic datasets and the characteristics of control subjects were used to construct classification
models such as Bagging, K-nearest neighbour (KNN), multilayer perceptron, and the support vector
machine (SVM). In the prepressing stage, the synthetic minority over-sampling technique (SMOTE)
with two-feature selection RFE and PCA were used. The PD dataset comprises a large difference
between the numbers of the infected and uninfected patients, which causes the classification bias
problem. Therefore, SMOTE was used to resolve this problem. Results: For model evaluation, the
train–test split technique was used for the experiment. All the models were Grid-search tuned, the
evaluation results of the SVM model showed the highest accuracy of 98.2%, and the KNN model
exhibited the highest specificity of 99%. Conclusions: the proposed method is compared with the
current modern methods of detecting Parkinson’s disease and other methods for medical diseases, it
was noted that our developed system could treat data bias and reach a high prediction of PD and this
can be beneficial for health organizations to properly prioritize assets.
Açıklama
Anahtar Kelimeler
Parkinson Detection, Machine Learning, PCA, RFE, SMOTE
Kaynak
Medicina
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
57
Sayı
11
Künye
Qasim, H. M., Ata, O., Ansari, M. A., Alomary, M. N., Alghamdi, S., & Almehmadi, M. (2021). Hybrid Feature Selection Framework for the Parkinson Imbalanced Dataset Prediction Problem. Medicina, 57(11), 1217.
