Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines

Eke, Chima S.; Jammeh, Emmanuel; Li, Xinzhong; Carroll, Camille; Pearson, Stephen; Ifeachor, Emmanuel

doi:10.1109/jbhi.2020.2984355

Cited by 68 publications

(36 citation statements)

References 52 publications

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“…Eke et.al. [28], presented a classification system of AD using gene expression datasets namely: GSE63060 and GSE63061. These two datasets were merged.…”

Section: Related Workmentioning

confidence: 99%

Alzheimer’s disease prediction using three machine learning methods

Ahmed

Kadhem

2022

IJEECS

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Alzheimer's disease (AD) is the most common incurable neurodegenerative illness, a term that encompasses memory loss as well as other cognitive abilities. The purpose of the study is using precise early-stage gene expression data from blood generated from a clinical Alzheimer's dataset, the goal was to construct a classification model that might predict the early stages of Alzheimer's disease. Using information gain (IG), a selection of characteristics was chosen to provide substantial information for distinguishing between normal control (NC) and early-stage AD participants. The data was divided into various sizes; three distinct machine learning (ML) algorithms were used to generate the classification models: support vector machine (SVM), Naïve Bayes (NB), and k-nearest neighbors (K-NN). Using the WEKA software tool and a variety of model performance measures, the capacity of the algorithms to effectively predict cognitive impairment status was compared and tested. The current findings reveal that an SVM-based classification model can accurately differentiate cognitively impaired Alzheimer's patients from normal healthy people with 96.6% accuracy. As discovered and validated a gene expression pattern in the blood that accurately distinguishes Alzheimer's patients and cognitively healthy controls, demonstrating that changes specific to AD can be detected far from the disease's core site.

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“…Eke et.al. [28], presented a classification system of AD using gene expression datasets namely: GSE63060 and GSE63061. These two datasets were merged.…”

Section: Related Workmentioning

confidence: 99%

Alzheimer’s disease prediction using three machine learning methods

Ahmed

Kadhem

2022

IJEECS

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“…In another study [ 23 ], the authors proposed a technique for detecting AD early using blood plasma proteins. The dataset was acquired from the ADNI portal, containing 146 blood plasma proteins from three clinical groups.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Preemptive Diagnosis of Alzheimer’s Disease in the Eastern Province of Saudi Arabia Using Computational Intelligence Techniques

Olatunji

Alansari

Alkhorasani

et al. 2022

Computational Intelligence and Neuroscience

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Alzheimer’s Disease (AD) is a silent disease that causes the brain cells to die progressively, influencing consciousness, behavior, planning ability, and language to name a few. AD increases exponentially with aging, where it doubles every 5-6 years, causing profound implications, such as swallowing difficulties and losing the ability to speak before death. According to the Ministry of Health in Saudi Arabia, AD patients will triple by 2060 to reach 14 million patients worldwide. The rapid rise of patients is caused by the silent progress of the disease, leading to late diagnosis as the symptoms will not be distinguished from normal aging affect. Moreover, with the current medical capabilities, it is impossible to confirm AD with 100% certainty via specific medical examinations. The literature review revealed that most recent publications used images to diagnose AD, which is insufficient for local hospitals with limited imaging capabilities. Other studies that used clinical and demographical data failed to achieve adequate results. Consequently, this study aims to preemptively predict AD in Saudi Arabia by employing machine learning (ML) techniques. The dataset was acquired from King Fahad Specialist Hospital (KFSH) in Dammam, Saudi Arabia, containing standard clinical tests for 152 patients. Four ML algorithms, namely, support vector machine (SVM), k-nearest neighbors (k-NN), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost), were employed to preemptively diagnose the disease. The empirical results demonstrated the robustness of SVM in the pre-emptive diagnosis of AD with accuracy, precision, recall, and area under the receiver operating characteristics (AUROC) of 95.56%, 94.70%, 97.78%, and 0.97, respectively, with 13 features after applying the sequential forward feature selection technique. This model can assist the medical staff in controlling the progression of the disease at low costs.

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“…By blood plasma protein which was easier to access and inexpensive to others, for early detection of AD 16 proteins are biomarkers correction based feature substance selection technique was used to select. These proteins biomarkers 2-degree polynomial kernel with SVM was used to classify AD [16]. DNA mythylation expression profiles were combined with genome-wide analysis to detect patients with AD.…”

Section: Ad Detection Machine Learning To Deep Learning a Machine Lea...mentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for detecting Alzheimer’s Disease (AD): A Review

Singh¹,

Kumar²

2022

IJERCSE

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Alzheimer's disease (AD) is the most prevalent chronic disease among the elderly, with a high prevalence. In the treatment of AD, early diagnosis of the patient plays an important role because of severe damage to the brain later. Deep learning (DL) and Machine Learning (ML) have gained popularity and success in the field of medical imaging in recent years. It has become the dominant way of assessing medical pictures, and it has also sparked considerable interest in the diagnosis of Alzheimer's disease. The deep and machine models are more precise and efficient for AD detection than ordinary machine learning techniques. This study provides AD-related biomarkers and feature extraction methods, discusses the use of the machine and deep learning approaches in AD detection, and analyses and summarises AD detection methodologies and models. The results suggest that DL and ML technology performs well in detecting AD.

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Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines

Cited by 68 publications

References 52 publications

Alzheimer’s disease prediction using three machine learning methods

Alzheimer’s disease prediction using three machine learning methods

Preemptive Diagnosis of Alzheimer’s Disease in the Eastern Province of Saudi Arabia Using Computational Intelligence Techniques

Machine Learning and Deep Learning Approaches for detecting Alzheimer’s Disease (AD): A Review

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