A Machine Learning Model to Predict the Onset of Alzheimer Disease using Potential Cerebrospinal Fluid (CSF) Biomarkers

Hassan, Syed Asif; Khan, Tabrej

doi:10.14569/ijacsa.2017.081216

Cited by 8 publications

(6 citation statements)

References 13 publications

(15 reference statements)

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“…Naganandhini and Shanmugavadivu (2019) proposed DTC for AD classification on the AD classification on OASIS dataset with an average accuracy obtained 99.10%. Hassan and Khan (2017) The LDA scoring method was used to fuse data from MRIs, PET, CSF, and genetic features. Experiments were conducted on the ADNI dataset, and the accuracy was achieved at 66.7% for three-way class classifications and 57.3% for four-way class classifications.…”

Section: Application Of Decision Tree In Alzheimermentioning

confidence: 99%

“…CSF (Hassan & Khan, 2017; Mofrad et al, 2019; Zhang et al, 2012) is a clear, watery fluid that flows in the spinal cord and brain acting like a cushion and protecting them from sudden shock or injury. The composition of CSF remains normal under normal circumstances but may show a modification in composition, quantity, and pressure when influenced by neurological disease.…”

Section: Modalities Used For Nddmentioning

confidence: 99%

“…I G U R E 1 2 Last four-year analysis of deep learning models for neurological disease detection.T A B L E 6 Potential biomarkers from neuroscience research to be exploited in machine learning field & Khan, 2017;Lin et al, 2021;Mofrad et al, 2019),. PD(Prashanth et al, 2016;Wang, Lee, et al…”

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confidence: 99%

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Machine learning approaches for neurological disease prediction: A systematic review

Fatima,

Masood

2024

Expert Systems

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In this article, we present a systematic and exhaustive review regarding the trends, datasets employed, as well as findings achieved in the last 11 years in neurological disorder prediction using machine learning models. In this work we present a comparison between the biomarkers used in ML field with the biomarkers that are obtained through other non‐ml‐based research fields. This will help in identifying the potential research gaps for ML domain. As the study of neurological disorders is a far‐reaching task due to the wide variety of diseases, hence the scope of this study is restricted to the three most prevalent neurological diseases, that is, Alzheimer's, Parkinson's, and Autism Spectrum Disorder (ASD). From our analysis, it has been found that over time deep learning techniques especially Convolutional Neural Networks have proved to be beneficial for the disease prediction task. For this reason, Magnetic Resonance Imaging have been a popular modality across all three considered diseases. It is also notable that the employment of a transfer learning approach and maintenance of a global data centre helps in dealing with data scarcity problems for model training. The manuscript also discusses the potential challenges and future scope in this field. To the best of our knowledge, unlike other studies, this work attempts to put forth a conclusion of every article discussed highlighting the salient aspects of the major studies for a particular problem.

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Section: Application Of Decision Tree In Alzheimermentioning

confidence: 99%

Section: Modalities Used For Nddmentioning

confidence: 99%

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Machine learning approaches for neurological disease prediction: A systematic review

Fatima,

Masood

2024

Expert Systems

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“…Using particular CSF biomarkers from an Alzheimer's clinical dataset, Hassan et al [20] proposed a classification model to predict earlier stages of AD. Several models were constructed and validated using various biomarkers to accurately predict the MCI status of a patient.…”

Section: Machine Learningmentioning

confidence: 99%

Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

Ahmed¹,

Soliman²,

El–Sappagh³

et al. 2023

Computer Systems Science and Engineering

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The precise diagnosis of Alzheimer's disease is critical for patient treatment, especially at the early stage, because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage occurs. It is possible to gain a holistic view of Alzheimer's disease staging by combining multiple data modalities, known as image fusion. In this paper, the study proposes the early detection of Alzheimer's disease using different modalities of Alzheimer's disease brain images. First, the preprocessing was performed on the data. Then, the data augmentation techniques are used to handle overfitting. Also, the skull is removed to lead to good classification. In the second phase, two fusion stages are used: pixel level (early fusion) and feature level (late fusion). We fused magnetic resonance imaging and positron emission tomography images using early fusion (Laplacian Re-Decomposition) and late fusion (Canonical Correlation Analysis). The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of each. Magnetic resonance imaging system's primary benefits are providing images with excellent spatial resolution and structural information for specific organs. Positron emission tomography images can provide functional information and the metabolisms of particular tissues. This characteristic helps clinicians detect diseases and tumor progression at an early stage. Third, the feature extraction of fused images is extracted using a convolutional neural network. In the case of late fusion, the features are extracted first and then fused. Finally, the proposed system performs XGB to classify Alzheimer's disease. The system's performance was evaluated using accuracy, specificity, and sensitivity. All medical data were retrieved in the 2D format of 256 × 256 pixels. The classifiers were optimized to achieve the final results: for the decision tree, the maximum depth of a tree was 2. The best number of trees for the random forest was 60; for the support vector machine, the maximum depth was 4, and the kernel gamma was 0.01. The system achieved an accuracy of 98.06%, specificity of 94.32%, and sensitivity of 97.02% in the case of early fusion. Also, if the system achieved late fusion, accuracy was 99.22%, specificity was 96.54%, and sensitivity was 99.54%.

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“…Patients diagnosed with amnestic mild cognitive impairment (MCI) at study baseline are at a higher risk for progression to dementia, but not all patients end up developing AD [13] . Research has been done to detect AD in patients with MCI or predict the early stage of AD using cerebrospinal fluid (CSF) biomarkers [14] , [15] , while others [16] have used psychometric and imaging data for predicting the progression of dementia in patients with amnestic MCI. In an implementation of a multiclass classifier using clinical and magnetic resonance (MR) brain images to classify controls, MCI and AD patients, an accuracy of 79.8% was achieved [17] .…”

Section: Introductionmentioning

confidence: 99%

Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning

Satone

Kaur

Dadu

et al. 2019

Preprint

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America 2 15 4 Data used in preparation of this article were obtained from the Alzheimer's Disease 16 Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the 17 ADNI contributed to the design and implementation of ADNI and/or provided data but did not 18 participate in analysis or writing of this report. A complete listing of ADNI investigators can be Abstract 33 Background 34 Alzheimer's disease (AD) is a common, age-related, neurodegenerative disease that impairs a 35 person's ability to perform day to day activities. Diagnosing AD is difficult, especially in the early 36 stages, many individuals go undiagnosed partly due to the complex heterogeneity in disease 37 progression. This highlights a need for early prediction of the disease course to assist its treatment 38 and tailor therapy options to the disease progression rate. Recent developments in machine 39 learning techniques provide the potential to not only predict disease progression and trajectory of 40 AD but also to classify the disease into different etiological subtypes. 41Methods and findings 42 The suggested work clusters participants in distinct and multifaceted progression subgroups of AD 43 and discusses an approach to predict the progression stage from baseline diagnosis. We observe 44 that the myriad of clinically reported symptoms summarized in the proposed AD progression space 45 corresponds directly to memory and cognitive measures, classically been used to monitor disease 46 onset and progression. The proposed work concludes notably accurate prediction of disease 47 progression after four years from the first 12 months of post-diagnosis clinical data (Area Under 48 the Curve of 0.92 (95% confidence interval (CI), 0.90-0.94), 0.96 (95% CI, 0.92-1.0), 0.90 (95% 49 CI, 0.86-0.94) and 0.83 (95% CI, 0.77-0.89) for controls, high, moderate and low progression rate 4 50 patients respectively). Further, we explore the long short-term memory (LSTM) neural networks 51 to predict the trajectory of a patient's progression. 52 Conclusion 53The machine learning techniques presented in this study may assist providers with identifying 54 different progression rates and trajectories in the early stages of disease progression, hence 55 allowing for more efficient and unique care deliveries. With additional information about the 56 progression rate of AD at hand, providers may further individualize the treatment plans. The 57 predictive tests discussed in this study not only allow for early AD diagnosis but also facilitate the 58 characterization of distinct AD subtypes relating to trajectories of disease progression. These 59 findings are a crucial step forward to early disease detection. Additionally, models can be used to 60 design improved clinical trials for AD research.61 Introduction 62 Alzheimer's disease (AD) is a progressive and age-associated, chronic neurodegenerative disease 63 affecting a patient's memory, intellectual skills, and other mental functions. It is the most common 64 form of...

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A Machine Learning Model to Predict the Onset of Alzheimer Disease using Potential Cerebrospinal Fluid (CSF) Biomarkers

Cited by 8 publications

References 13 publications

Machine learning approaches for neurological disease prediction: A systematic review

Machine learning approaches for neurological disease prediction: A systematic review

Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning

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