Diagnosis of Alzheimer’s Disease using Wrapper Feature Selection Method
0
조회
0
다운로드
3
인용
0
참고문헌
키워드
초록
Alzheimer’s disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.
Abstract
Alzheimer’s disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.
참고문헌 (18)
- J. A. M. Sidey-Gibbons, "Machine learning in medicine : a practical introduction", BMC Med. Res. Methodol, 19(1), pp.64~ (2019)
- A. Association, "2015 Alzheimer’s disease facts and figures", Alzheimers Dement, 11(3), pp.332~384 (2015)
- R. A. Sperling, "Toward defining the preclinical stages of Alzheimer’s disease : Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease", Alzheimers Dement, 7(3), pp.280~292 (2011)
- B. C. Dickerson, "The Cortical Signature of Alzheimer’s Disease : Regionally Specific Cortical Thinning Relates to Symptom Severity in Very Mild to Mild AD Dementia and is Detectable in Asymptomatic Amyloid-Positive Individuals", Cereb. Cortex, 19(3), pp.497~510 (2009)
- G. Mirzaei, "Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia", Biomed. Signal Process. Control, 72, pp.103293~ (2022)
- T. M. Schouten, "Individual classification of Alzheimer’s disease with diffusion magnetic resonance imaging", NeuroImage, 152, pp.476~481 (2017)
- A. H. Syaifullah, "Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation", Front. Neurol, 11 (2021)
- H. Kaur, "Predictive modelling and analytics for diabetes using a machine learning approach", Appl. Comput. Inform, 18(1, pp.2) (2020), 90~100
- R. Prajapati, "An Efficient Deep Neural Network Binary Classifier for Alzheimer’s Disease Classification", 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp.231~234 (2021)
- S. Basheera, "A novel CNN based Alzheimer’s disease classification using hybrid enhanced ICA segmented gray matter of MRI", Comput. Med. Imaging Graph, 81, pp.101713~ (2020)
- R. K. Lama, "Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features", J. Healthc. Eng, 2017, pp.e5485080~ (2017)
- Fazal Ur Rehman Faisal, "Diagnosis of Alzheimer's Disease using Combined Feature Selection Method", 멀티미디어학회논문지, 24(5), pp.667~675 (2021), 한국멀티미디어학회
- H. Das, "A Jaya algorithm based wrapper method for optimal feature selection in supervised classification", J. King Saud Univ. - Comput. Inf. Sci, 34(6), pp.3851~3863 (2022)
- R. K. Lama, "Diagnosis of Alzheimer’s Disease Using Brain Network", Front. Neurosci, 15 (2021)
- Md. K. Hasan, "Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers", IEEE Access, 8, pp.76516~76531 (2020)
- S. Alam, "Performance of classification based on PCA, linear SVM, and Multi-kernel SVM", 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), pp.987~989 (2016)
- M. Liu, "Joint Binary Classifier Learning for ECOC-Based Multi-Class Classification", IEEE Trans. Pattern Anal. Mach. Intell, 38(11), pp.2335~2341 (2016)
- M. Grandini, "Metrics for Multi-Class Classification: an Overview" (2020)