Accuracy of Support-Vector Machines for Diagnosis of Alzheimer's Disease, Using Volume of Brain Obtained by Structural MRI at Siriraj Hospital

Vichianin, Yudthaphon; Khummongkol, Anutr; Chiewvit, Pipat; Raksthaput, Atthapon; Chaichanettee, Sunisa; Aoonkaew, Nuttapol; Senanarong, Vorapun

doi:10.3389/fneur.2021.640696

Cited by 24 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SVM modeling has proven effective in various applications and has recently gained momentum within healthcare and medicine. 59,60,61 This method has previously been employed for the detection of various diseases such as Diabetes, 62 Alzheimer's, 63,64 Psoriasis, 65 Hepatitis, 66 and more. 67,68 Utilizing this SVM ML model, we examined the ability of DNA-SWCNTs to identify differing cell phenotypes as a function of DNA length.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Assisted Spectral Fingerprinting for Immune Cell Phenotyping

Nadeem,

Lyons,

Kindopp

et al. 2024

Preprint

View full text Add to dashboard Cite

Spectral fingerprinting has emerged as a powerful tool, adept at identifying chemical compounds and deciphering complex interactions within cells and engineered nanomaterials. Using near-infrared (NIR) fluorescence spectral fingerprinting coupled with machine learning techniques, we uncover complex interactions between DNA-functionalized single-walled carbon nanotubes (DNA-SWCNTs) and live macrophage cells, enabling in situ phenotype discrimination. Through the use of Raman microscopy, we showcase statistically higher DNA-SWCNT uptake and a significantly lower defect ratio in M1 macrophages as compared to M2 and naive phenotypes. NIR fluorescence data also indicate that distinctive intra-endosomal environments of these cell types give rise to significant differences in many optical features such as emission peak intensities, center wavelengths, and peak intensity ratios. Such features serve as distinctive markers for identifying different macrophage phenotypes. We further use a support vector machine (SVM) model trained on SWCNT fluorescence data to identify M1 and M2 macrophages, achieving an impressive accuracy of > 95%. Finally, we observe that the stability of DNA-SWCNT complexes, influenced by DNA sequence length, is a crucial consideration for applications such as cell phenotyping or mapping intra-endosomal microenvironments using AI techniques. Our findings suggest that shorter DNA-sequences like GT6 give rise to more improved model accuracy (> 87%) due to increased active interactions of SWCNTs with biomolecules in the endosomal microenvironment. Implications of this research extend to the development of nanomaterial-based platforms for cellular identification, holding promise for potential applications in real time monitoring of in vivo cellular differentiation.

show abstract

Section: Resultsmentioning

confidence: 99%

Machine Learning Assisted Spectral Fingerprinting for Immune Cell Phenotyping

Nadeem,

Lyons,

Kindopp

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…SVM uses the kernel trick, which maps the features to higher dimensional space to find the appropriate training model that can be generalized. The polynomial kernel is exploited in this study [40][41][42]. Figure 12 illustrates the principle of SVM.…”

Section: Feature Weighting Methodsmentioning

confidence: 99%

A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images

Alsalatie,

Alquran,

Mustafa

et al. 2023

Diagnostics

View full text Add to dashboard Cite

One of the most widespread health issues affecting women is cervical cancer. Early detection of cervical cancer through improved screening strategies will reduce cervical cancer-related morbidity and mortality rates worldwide. Using a Pap smear image is a novel method for detecting cervical cancer. Previous studies have focused on whole Pap smear images or extracted nuclei to detect cervical cancer. In this paper, we compared three scenarios of the entire cell, cytoplasm region, or nucleus region only into seven classes of cervical cancer. After applying image augmentation to solve imbalanced data problems, automated features are extracted using three pre-trained convolutional neural networks: AlexNet, DarkNet 19, and NasNet. There are twenty-one features as a result of these scenario combinations. The most important features are split into ten features by the principal component analysis, which reduces the dimensionality. This study employs feature weighting to create an efficient computer-aided cervical cancer diagnosis system. The optimization procedure uses the new evolutionary algorithms known as Ant lion optimization (ALO) and particle swarm optimization (PSO). Finally, two types of machine learning algorithms, support vector machine classifier, and random forest classifier, have been used in this paper to perform classification jobs. With a 99.5% accuracy rate for seven classes using the PSO algorithm, the SVM classifier outperformed the RF, which had a 98.9% accuracy rate in the same region. Our outcome is superior to other studies that used seven classes because of this focus on the tissues rather than just the nucleus. This method will aid physicians in diagnosing precancerous and early-stage cervical cancer by depending on the tissues, rather than on the nucleus. The result can be enhanced using a significant amount of data.

show abstract

“…For instance, SVMs are particularly effective in classifying complex datasets with a high-dimensional space, making them suitable for MRI and PET scan analysis. (Habehh & Gohel, 2021) (Vichianin et al, 2021)…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Predicting Alzheimer's Disease Using Artificial Intelligence and Machine Learning: A Comprehensive Analysis

Sekhar

2024

Preprint

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is a significant global health challenge, characterized by progressive neurodegeneration leading to severe cognitive decline. Early diagnosis of AD is crucial for effective management and treatment, yet it remains elusive due to the disease’s complex etiology and symptomatology. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) offer promising avenues for early prediction and diagnosis of AD. This paper explores various AI and ML methodologies applied to predicting Alzheimer’s disease, focusing on their roles in analyzing neuroimaging, genetic data, and electronic health records (EHR). Machine learning models, such as Support Vector Machines (SVM) and deep learning techniques, including Convolutional Neural Networks (CNN), are examined for their effectiveness in detecting early signs of AD. Natural Language Processing (NLP) methods are also discussed for their potential in analyzing speech and language patterns associated with cognitive decline. The paper highlights the challenges of integrating these technologies into clinical practice, such as data privacy concerns, algorithmic bias, and the need for significant computational resources. Additionally, it proposes future research directions, emphasizing the importance of multi-modal data integration, cloud-based solutions for scalability, and ethical considerations. Through this comprehensive analysis, the paper aims to demonstrate the transformative potential of AI and ML in improving early detection and management of Alzheimer’s disease, ultimately enhancing patient outcomes and reducing the societal burden of AD.

show abstract

Accuracy of Support-Vector Machines for Diagnosis of Alzheimer's Disease, Using Volume of Brain Obtained by Structural MRI at Siriraj Hospital

Cited by 24 publications

References 24 publications

Machine Learning Assisted Spectral Fingerprinting for Immune Cell Phenotyping

Machine Learning Assisted Spectral Fingerprinting for Immune Cell Phenotyping

A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images

Predicting Alzheimer's Disease Using Artificial Intelligence and Machine Learning: A Comprehensive Analysis

Contact Info

Product

Resources

About