A comparative study on various machine learning approaches for the detection of Alzheimer disease (original) (raw)
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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.
Springer, 2019
According to the Dementia India report 2010, it is estimated that over 3.7 million people are affected by dementia and is expected to be double by 2030. Around 60-80% of the demented are suffering from Alzheimer's disease. Neuropsy-chological tests are useful tools for diagnosis of dementia. Diagnosis of dementia using machine learning for low-and middle-income setting is a rare study. Various attributes are used for diagnosing dementia. Finding the prominent attributes among them is a tedious job. Chi-squared, gain ratio, info gain and ReliefF filtering techniques are used for finding the prominent attributes. Cognitive score is identified as the most prominent attribute.
Computer Engineering and Applications Journal, 2016
Alzheimer's disease (AD) is the most common type of dementia in the elderly. Approximately, 26 million people worldwide are affected by AD. Among the various diagnostic methods for Alzheimer's disease, MRI brain imaging can display sharp changes in brain tissues. It can be used as a method for early diagnosis of Alzheimer's disease. Considering the high volume of features related to brain tissue thickness, requires the using feature reduction methods. For this purpose, statistical tests pair sample test and Independent sample test was used. After careful selection of key features, for reducing the number of features, SAS which is a kernel-based feature selection algorithm is used in linear and nonlinear mode. At the end, neural network classification, decision trees, nearest neighbor and Naïve Bayes algorithms are used for modeling. Results show that the classification accuracy of obtained feature subsets have better results compare to the original data set.
A Survey of Different Machine Learning Models for Alzheimer Disease Prediction
International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), 2020
Machine learning model is one of the best disease prediction framework in various medical disease prediction processes. Alzheimer’s disease (AD) is a progressive neuro-degenerative condition with different severity features. However, it is noted that very few patients who is suffering from Alzheimer’s disease are decided to take correct clinical decision making. Most of the traditional machine learning models help to detect the AD with limited feature space and dimensionality. Also, these models are not applicable to high dimensional features due to sparsity problem. Several high dimensional classification and clustering methods have recently been proposed to predict the AD automatically. Component selection plays a significant role in improving the performance of these programs. Therefore, various forms of feature selection techniques are analyzed in this survey article. The purpose of the paper is to include an analytical overview and strategic examination of the latest research work performed using Machine Learning Strategies to early diagnosis of AD.
Selecting and Extracting Effective Features for Automated Diagnosis of Alzheimer's Disease
International Journal of Computer Applications, 2013
In this paper, a Computer Aided Diagnosis (CAD) system is proposed to provide a comprehensive analytic method for extracting the most significant features of Alzheimer's disease (AD). It consists of three stages: feature selection, feature extraction, and classification. This proposal selects the features that have different intensity level at all images and discarding the features that have the same intensity level to reach the fewer subset of features that have the most impact distinctive of AD. Then reduces the features by proposing a new feature extraction algorithm that minimizes intra separately distance of AD features. Finally, a Linear Support Vector Machine (SVM) classifier was used to perform binary classifications among AD patients. The data set that used for testing the proposed model consists of 120 cross-sectional Structural MRI images from the Open Access Series of Imaging Studies (OASIS) database. Experiments have been conducted on Open Access Series of Imaging Studies (OASIS) database. The results show that the highest classification performance is obtained using the proposed model, and this is very promising compared to Principle Component Analysis (PCA) and Linear Discriminate Analysis (LDA).
Dementia Prediction Using Machine Learning
Dementia is a chronic and degenerative condition, which has become a major health concern among the elderly. With ever-continuing cases of dementia, it has become a very challenging task in the 21st century to provide care for patients with dementia. This paper proposes a framework for the prediction of dementia using the data collected from the OASIS (Open Access Series of Imaging Studies) project which was made available by the Washington University Alzheimer's Disease Research Centre. Different techniques have been implemented for data imputation, preprocessing and data transformation to create suitable data for training the model. Machine learning approaches like Adaboost (AB), Decision Tree (DT), Extra Tree (ET), Gradient Boost (GB), K-Nearest Neighbour (KNN), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), and SVM (Support Vector Machine) has been used for a combination of features. These techniques have been applied to the full set of features and features selected from Least Absolute Shrinkage and Selection Operator (LASSO) techniques. A comparison between the accuracy, precision, and other metrics based on the results of the classification algorithms has been provided. The experimental results show that the highest accuracy of 96.77% was obtained by Support Vector Machine (SVM) with full features. The proposed methodology is promising and if developed and deployed can be helpful for the rapid assessment of Alzheimer's Disease (AD).
Performance Study Of Machine Learning Algorithms Used For Alzheimer’s Disease Detection
Journal of Pharmaceutical Negative Results
Dementia is widely recognized. With age comes a dramatic surge in dementia cases. It is an irreversible brain disorder that impairs thinking, memory, and judgment, causing a person’s cognitive ability to decline. Around 50 million individuals worldwide have dementia, and 10 million new cases are identified yearly. Therefore, solving this problem has become urgently necessary, and dementia must be diagnosed early for more advanced treatments to develop. Cognitive tests are used to assess a person’s mental capacity to diagnose this condition early. In the present study, we tried to detect dementia in its early stages using machine learning approaches. Data collected for the analysis comprised gender, age, education, MMSE (Mini‐Mental State Examination), CDR (Clinical Dementia Rating), ASF (Atlas scaling factor), handedness, and hospital visits for patients classified as demented or non-demented. We applied machine learning approaches such as KNN, DT (Decision Tree), and RF (Random For...
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
Logistic random forest boosting technique for Alzheimer’s diagnosis
International Journal of Information Technology
Alzheimer's disease (AD) is a common and wellknown neurodegenerative condition that causes cognitive impairment. In the field of medicine, it is the "nervous system" disorder that has received the most attention. Despite this extensive research, there is no treatment or strategy to slow or stop its spread. Nevertheless, there are a variety of options (medication and non-medication alternatives) that may aid in the treatment of AD symptoms at their various phases, thereby enhancing the patient's quality of life. As AD advances over time, it is necessary to treat patients at their various stages appropriately. As a result, detecting and classifying AD phases prior to symptom treatment can be beneficial. Approximately twenty years ago, the rate of progress in the field of machine learning (ML) accelerated dramatically. Using ML methods, this study focuses on early AD identification. The "Alzheimer's Disease Neuroimaging Initiative" (ADNI) dataset was subjected to exhaustive testing for AD identification. The purpose was to classify the dataset into three groups: AD, "Cognitive Normal" (CN), and "Late Mild Cognitive Impairment" (LMCI). In this paper, we present the ensemble model Logistic Random Forest Boosting (LRFB), representing the ensemble of "
Filter Feature Selection Analysis to Determine the Characteristics of Dementia
2021
Alzheimer's disease Dementia Filter feature selection ADNI Freesurfer Dementias are known as neuropsychiatric disorders. Two-dimensional sliced brain scans can be generated via magnetic resonance imaging. Three-dimensional measurements of regions can be reached from those scans. Numerical brain features can be extracted through operating the Freesurfer tool. Parametrizing those features and demographic information in learning algorithms can label an unknown sample as healthy or dementia. On the other hand, some of the features in the initial set may be less practical than others. In this research, the aim is to decrease the input feature-count, a total of 2939 attributes, as a first step to determine the most distinctive dementia characteristics. To that end, a total of 2264 ADNI dataset samples (471 AD, 428 lMCI, 669 eMCI, 696 healthy controls) are divided into two sets: 65% training set (1464 samples) and 35% test set (800 samples). Various filter feature selection algorithms (Information Gain, Gain Ratio, Symmetrical Uncertainty, Pearson's Correlation, Correlation-based Feature Subset Selection) are tested over different parameters together with multiple Bayesian-based and tree-based classifiers. Test performance accuracy rates up to 76.50% are analyzed in detail. Instead of processing the whole feature set, the overall performance tends to increase with correctly fewer attributes taken.