Using Machine Learning Methods for Detecting Alzheimer's Disease through Hippocampal Volume Analysis

Uysal, Gokce; Öztürk, Muzaffer

doi:10.1109/tiptekno.2019.8895135

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…During the training process, an SA provides feedback about the prediction accuracy. SA is widely used in data classification process for different applications such as early detection and prediction of diabetes [ 24 , 25 , 26 , 27 ], prediction of Alzheimer’s Disease [ 28 , 29 , 30 , 31 ], detection of Acute Respiratory Distress Syndrome [ 32 , 33 , 34 ] and EEG Signal Processing [ 35 , 36 , 37 , 38 ].…”

Section: Supervised Machine-learning Approachesmentioning

confidence: 99%

EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data

et al. 2022

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There has recently been increasing interest in postural stability aimed at gaining a better understanding of the human postural system. This system controls human balance in quiet standing and during locomotion. Parkinson’s disease (PD) is the most common degenerative movement disorder that affects human stability and causes falls and injuries. This paper proposes a novel methodology to differentiate between healthy individuals and those with PD through the empirical mode decomposition (EMD) method. EMD enables the breaking down of a complex signal into several elementary signals called intrinsic mode functions (IMFs). Three temporal parameters and three spectral parameters are extracted from each stabilometric signal as well as from its IMFs. Next, the best five features are selected using the feature selection method. The classification task is carried out using four known machine-learning methods, KNN, decision tree, Random Forest and SVM classifiers, over 10-fold cross validation. The used dataset consists of 28 healthy subjects (14 young adults and 14 old adults) and 32 PD patients (12 young adults and 20 old adults). The SVM method has a performance of 92% and the Dempster–Sahfer formalism method has an accuracy of 96.51%.

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Section: Supervised Machine-learning Approachesmentioning

confidence: 99%

EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data

et al. 2022

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“…For AD recognition, multiple biomarkers have been discovered in the literature. Methodologies developed in [8] [9][10] [11][12][13][14][5] [15] using MRI images to treat AD.The methodology used is [7] is based on consecutive amino acid that provides sequence for Alzheimer disease detection. Sequence provide by this method is given to SVM classifier and results are generated.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Alzheimer’s Disease using Comparative Study on Machine Learning Models

Zohra

Akram²,

Din

2021

VFAST trans. softw. eng.

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The method of diagnosing and treating diseases can be improved by identifying the genes that cause diseases. Alzheimer’s disease (AD) is one of the neurodegenerative disease that slowly destroys memory as well as thinking abilities. It’s important to diagnose Alzheimer’s disease (AD) early on so that adequate treatment can be given to patient. That article compares various machine learning models for identify Alzheimer’s Disease and proves that which algorithm gives the most reliable results in detecting AD in advance. Machine learning is a backbone of technology and everything in our life related to machine learning technologies. In this study various biomarkers are developed based on different machine learning classifiers like Random Forest, K-NN, Support Vector Machine, AdaBoost and XgBoost for AD gene detection. Genome data is extracted from NCBI related to Alzheimer disease. After that features are extracted from this genome data. Then above machine learning classifiers are train on these features. Different results are obtained by using Self-Consistency test and 10 Cross Validation test. Random Forest in both test gives 100% results. KNN gives 73.17% and 86.33%, SVM gives 100% and 97% AdaBoost gives 74.02% and 87.42%, XgBoost gives 86.04%and 92.56%accuracy for self-consistency and 10 Cross Validation test respectively.

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“…Use of different machine learning (ML) algorithms in analyzing bio-medical images is quite common since last decade. Different ML algorithms, e.g., discrete wavelet transform [33] and principal component analysis [17,34,35] have been used to identify significant features and K-Means [1] and Fuzzy C-Means [33,36] for clustering, where Random Forest [37,38], K-nearest neighbor [39,40], and support vector machine (SVM) [16,34,41,42] have been used for classifying data. Artificial neural network and convolutional neural network [18,[43][44][45][46] have been found to be widely used for identifying biomarkers and classifying bio-medical images.…”

Section: Introductionmentioning

confidence: 99%

“…However, these algorithms require powerful graphics and high-speed processors and take comparatively more time to be executed. Studies have also compared performance of different ML algorithms to find out optimized predictive model [37,41,50].…”

Section: Introductionmentioning

confidence: 99%

Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer’s Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques

Das

Panigrahi

Chakrabarti

2021

ADR

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Background: The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease. Objective: To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques. Methods: We have performed automatic detection and segmentation of the CC and calculate its morphological features to feed into a multivariate pattern analysis using support vector machine learning techniques. Results: Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients. Conclusion: Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.

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Using Machine Learning Methods for Detecting Alzheimer's Disease through Hippocampal Volume Analysis

Cited by 11 publications

References 9 publications

EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data

EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data

Diagnosis of Alzheimer’s Disease using Comparative Study on Machine Learning Models

Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer’s Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques

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