A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects (original) (raw)
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IEEE
Alzheimer's disease has recently become a big worry. This condition affects around 45 million people worldwide. Alzheimer's disease is a degenerative brain illness that primarily affects old adults and has no known cause or pathogenesis. Dementia is the major cause of Alzheimer's disease, which kills brain cells over time. This sickness took away people's capacity to think, read, and do many other things. By forecasting the sickness, a machine learning system can help solve this problem. The major goal is to identify Dementia in a variety of patients. This study covers the results and analyses of multiple machine learning models for diagnosing dementia. ADNI & Kaggle dataset was used. dataset is nearly 6000 MRI Images, but it contains four classes Mild demented, demented, moderate demented, very mild demented. Dataset was evaluated and used in a variety of machine learning models. For prediction, support vector machines, logistic regression, decision trees, and random forests were employed. The system was ran without fine-tuning first, and then with fine-tuning afterwards. When the results are compared, it is discovered that the support vector machine produces the best outcomes of all the models. It offers the highest sensitivity for diagnosing dementia in a large number of individuals. This system is simple and can quickly assist individuals in diagnosing Dementia..
Performances of Machine Learning Models for Diagnosis of Alzheimer’s Disease
Annals of Data Science
In recent times, various machine learning approaches have been widely employed for effective diagnosis and prediction of diseases like cancer, thyroid, Covid-19, etc. Likewise, Alzheimer's (AD) is also one progressive malady that destroys memory and cognitive function over time. Unfortunately, there are no dedicated AI-based solutions for diagnoses of AD to go hand in hand with medical diagnosis, even though multiple factors contribute to the diagnosis, making AI a very viable supplementary diagnostic solution. This paper reports an endeavor to apply various machine learning algorithms like SGD, k-Nearest Neighbors, Logistic Regression, Decision tree, Random Forest, AdaBoost, Neural Network, SVM, and Naïve Bayes on the dataset of affected victims to diagnose Alzheimer's disease. Longitudinal collections of subjects from OASIS dataset have been used for prediction. Moreover, some feature selection and dimension reduction methods like Information Gain, Information Gain Ratio, Gini index, Chi-Squared, and PCA are applied to rank different factors and identify the optimum number of factors from the dataset for disease diagnosis. Furthermore, performance
Diagnosis of Alzheimer's Disease using Machine Learning Algorithms
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and is considered to be the main cause of cognitive impairment in elderly people. It is a progressive disease that destroys memory and other important mental functions and causes problems with memory, thinking and behavior. Symptoms usually develop slowly and worsen over time Symptoms may become severe enough to interfere with daily life, and lead to death. In 2022, 55 million people worldwide suffered from this disease. AD is predicted to affect 1 in 85 people globally by 2050, and at least 43% of prevalent cases need a high level of care. Alzheimer's Disease Neuroimaging Initiative (ADNI) give datasets that can be utilized for different Alzheimer's Disease related examinations. The dataset consists of a longitudinal MRI data of 150 subjects aged 60 to 96.72 of the subjects were grouped as 'Nondemented' throughout the study.64 of the subjects were grouped as 'Demented' at the time of their initial visits and remained so throughout the study.14 subjects were grouped as 'Nondemented' at the time of their initial visit and were subsequently characterized as 'Demented' at a later visit. These fall under the 'Converted' category. In our project, we propose some machine learning models to detect the Alzheimer's disease in earlier stage by finding the accuracy levels and determining the attributes that helps us to find the maximum accuracy rate. I.
Journal of Multimedia Information System
This paper illustrates a comparative analysis of performance of 4 machine learning algorithms i.e. LDA, Naive Bayes (NB), k-Nearest Neighbours (KNN) and Support Vector Machines (SVM) on the basis of their classification accuracy. The whole paper is divided into 7 sections, i.e. introduction, literature review, data preprocessing, methodology, results and discussion, conclusion and finally the future scope. This section gives brief introduction about the field and its area of application, next section gives a brief review of the corresponding literature, followed by data preprocessing, methodology & experimentation, results and discussion, conclusion and the future scope.
Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer’s Case Study
Mathematics
Alzheimer’s Disease (AD) is a highly prevalent condition and most of the people suffering from it receive the diagnosis late in the process. The diagnosis is currently established following an evaluation of the protein biomarkers in cerebrospinal fluid (CSF), brain imaging, cognitive tests, and the medical history of the individuals. While diagnostic tools based on CSF collections are invasive, the tools used for acquiring brain scans are expensive. Taking these into account, an early predictive system, based on Artificial Intelligence (AI) approaches, targeting the diagnosis of this condition, as well as the identification of lead biomarkers becomes an important research direction. In this survey, we review the state-of-the-art research on machine learning (ML) techniques used for the detection of AD and Mild Cognitive Impairment (MCI). We attempt to identify the most accurate and efficient diagnostic approaches, which employ ML techniques and therefore, the ones most suitable to b...
International Journal of Biomedical Science and Engineering, 2017
The most dominant form of dementia, memory loss, is Alzheimer's disease (AD). Imaging is important for monitoring, diagnosis, and education of Alzheimer's disease prediction. Automated classification of subjects could provide support for clinicians. This study examined two classification methods to separate among elderly persons with normal cognitive (NC), Alzheimer's disease (AD), and mild cognitive impairment (MCI) by using images from the magnetic resonance imaging (MRI). The dataset consists of 120 subjects separated into 40 ADs, 40 MCIs, and 40 NCs. The first technique was K-Nearest Neighbor (KNN) and the second technique was Support Vector Machine (SVM), firstly all the subjects were filtered and normalized, secondly twelve features were extracted. After feature selection, two techniques of classification were examined with Permutations and combinations for all features in order to select the best features which have the highest accuracy for identification the classes. The best average accuracy was 97.92% using SVM polynomial order three, and best all average accuracy was 95.833% using KNN with K=6, and K=7 for random selection of testing data with SVM and KNN. The results show a relatively high classification accuracy between the three clinical categories. In summary, the proposed automatic classification technique can be used as a noninvasive diagnostic tool for Alzheimer's disease, with the capability of defining early stages of the disease.
International Journal of Biomedical Science and Engineering, 2016
The most dominant form of dementia, memory loss, is Alzheimer's disease (AD). Imaging is important for monitoring, diagnosis, and education of Alzheimer's disease prediction. Automated classification of subjects could provide support for clinicians. This study examined two classification methods to separate among elderly persons with normal cognitive (NC), Alzheimer's disease (AD), and mild cognitive impairment (MCI) by using images from the magnetic resonance imaging (MRI). The dataset consists of 120 subjects separated into 40 ADs, 40 MCIs, and 40 NCs. The first technique was K-Nearest Neighbor (KNN) and the second technique was Support Vector Machine (SVM), firstly all the subjects were filtered and normalized, secondly twelve features were extracted. After feature selection, two techniques of classification were examined with Permutations and combinations for all features in order to select the best features which have the highest accuracy for identification the classes. The best average accuracy was 97.92% using SVM polynomial order three, and best all average accuracy was 95.833% using KNN with K=6, and K=7 for random selection of testing data with SVM and KNN. The results show a relatively high classification accuracy between the three clinical categories. In summary, the proposed automatic classification technique can be used as a noninvasive diagnostic tool for Alzheimer's disease, with the capability of defining early stages of the disease.
An Optimized Machine Learning Framework for Detecting Alzheimer's Disease By MRI
JOURNAL OF ALGEBRAIC STATISTICS, 2022
Machine learning has extensive application in diverse medical fields.With advancements in medical technologies, access has been given to data for the identification of diseases in theirearly stages. Alzheimer's Disease (AD) is a chronic illnessthat will cause degeneration of the brain cells and ultimately will lead to memoryloss. AD causedcognitive mental problems like forgetfulness and confusion, as well as other symptoms such aspsychologicaland behavioralproblems, are further recommended to undergo test procedures usingneuroimagingtechniques. This work's objective is to utilize the machinelearning algorithms for processing the data acquired via neuroimaging technologies for early-stage AD detection. The framework extracts featuresusingcurvelet transform from MRI brain image. This work will also present the Decision Tree, the Adaptive Boosting (AdaBoost), and the Extreme Gradient Boosting (XGBoost) classifiers. In machine learning, Population-Based Incremental Learning (PBIL) is an optimization algorithm, in spite of being simpler than a conventional genetic algorithm, the PBIL algorithm is able to achieve much better results in several cases.PBIL is used to optimize the AdaBoost and XGBoost classifiers to improve AD classification. The experimental outcomes will demonstrate the proposed approach's superior performance over that of other existing approaches.
A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer’s Disease
Journal of Healthcare Engineering, 2021
Alzheimer's disease has been one of the major concerns recently. Around 45 million people are suffering from this disease. Alzheimer's is a degenerative brain disease with an unspecified cause and pathogenesis which primarily affects older people. e main cause of Alzheimer's disease is Dementia, which progressively damages the brain cells. People lost their thinking ability, reading ability, and many more from this disease. A machine learning system can reduce this problem by predicting the disease. e main aim is to recognize Dementia among various patients. is paper represents the result and analysis regarding detecting Dementia from various machine learning models. e Open Access Series of Imaging Studies (OASIS) dataset has been used for the development of the system. e dataset is small, but it has some significant values. e dataset has been analyzed and applied in several machine learning models. Support vector machine, logistic regression, decision tree, and random forest have been used for prediction. First, the system has been run without fine-tuning and then with fine-tuning. Comparing the results, it is found that the support vector machine provides the best results among the models. It has the best accuracy in detecting Dementia among numerous patients. e system is simple and can easily help people by detecting Dementia among them.
Alzheimer’s Disease (AD) is a degenerative brain disease and is the most common cause of dementia. Despite being a common disease, AD is poorly understood. Current medical treatments for AD are aimed at slowing the progression of the disease. So early detection of AD is important to intervene at an early stage of the disease. In recent years, by using machine learning predictive algorithms, assisted clinic diagnosis has received great attention due to its success of machine learning advances in the domains of computer vision. In this study, we have combined brain MRI imaging features and the features of other datatypes, and adopted various models, including XGBoost, logistic regression, and k-Nearest Neighbors, to improve AD diagnosis. We evaluated the models on the benchmark dataset of Alzheimer’s Disease Neuroimaging Initiative. Experiment results show that the logistic regression model is the top performer in terms of evaluation metrics of precision, recall, and F1-score. The pre...