[In Press] Early detection of Alzheimer's disease using cognitive features : a voting-based ensemble machine learning approach

Early detection of Alzheimer's Disease (AD) is vital for adequate control. Machine learning techniques have gained much attraction due to their efficiency in predicting AD using cognitive tests. Ensemble machine learning models are helpful in improving the robustness of the learning system via combining multiple machine learning models. This paper proposes a novel ensemble machine learning technique for the early detection of AD. Firstly, a novel feature selection technique referred to as Neighborhood Component Analysis and Correlation-based Filtration (NCA-F) is proposed to select the vital cognitive features from a given dataset. Secondly, various machine learning classifiers were trained using the proposed NCA-F method. The top classifiers were selected for voting based on the performance results. The voting is performed using an adaptive weight matrix process. The output label of a model is multiplied by the F1 score and represented as weight. The results revealed an accuracy of 93.92% when using adaptive voting, which is better than the accuracy of 90.53% observed when using the traditional artificial neural network (ANN) method. The proposed technique improved accuracy of detecting AD at early stage. Furthermore, the results against a recent study using same features also revealed an improvement of 12.12% in accuracy

Early detection of the Alzheimer's disease : a novel cognitive feature selection approach using machine learning

Alzheimer’s Disease (AD) is a dynamic condition that affects cognitive capabilities and functioning. It is a challenging disease to detect, particularly in its early stages. Early diagnosis of AD is the key for its treatment and slowing of its progress. This paper argues and clearly shows the benefits of using cognitive tests for efficient and early AD detection. In this study, a novel approach for the early detection of AD is proposed. We refer to it as Neighborhood Component Analysis and Correlation-based Filtration (NCA-F) and is based on selecting and identifying significant cognitive features. Cognitive features are used to train four Machine Learning (ML) classifiers, including Support Vector Machine (SVM), Naïve Bayes (NB), ANN, and AdaBoost Ensemble (AdB). Our analysis shows that the proposed approach can achieve an 88% classification accuracy. In addition, the performance of various ML classifiers with varying combinations of features has been studied. The proposed feature selection approach that implements AdB is seen to provide the best performance by various metrics

Enhancing early dementia detection : a machine learning approach leveraging cognitive and neuroimaging features for optimal predictive performance

Dementia, including Alzheimer’s Disease (AD), is a complex condition, and early detection remains a formidable challenge due to limited patient records and uncertainty in identifying relevant features. This paper proposes a machine learning approach to address this issue, utilizing cognitive and neuroimaging features for training predictive models. This study highlighted the viability of cognitive test scores in dementia detection—a procedure that offers the advantage of simplicity. The AdaBoost Ensemble model, trained on cognitive features, displayed a robust performance with an accuracy rate of approximately 83%. Notably, this model surpassed benchmark models such as the Artificial Neural Network, Support Vector Machine, and Naïve Bayes. This study underscores the potential of cognitive tests and machine learning for early dementia detection

BEGSS! : pruning candidate selection for ConvNets

Deep learning has been used to get human-level performance on classification on some of the renowned problems like speech and object recognition. It is very difficult to port deep learning algorithms to resource-limited devices because of their computational cost. However, many ideas have been proposed by the researchers to reduce the computational cost and in this work, we addressed the same problem. We propose one of the promising techniques i.e., Pruning. Pruning starts with learning a large-sized network and then removing the least adversarial parameters. Since in feature map pruning, all the outgoing and incoming kernels are removed, which affects the parameters. Therefore, achieving a high pruning ratio is difficult. To achieve a high pruning ratio, we need to select the candidate for pruning intelligently. Therefore, we propose BEGSS! the pruning candidate selection based on Best of random, Entropy, Gray threshold, Sparsity, and Signal strength. We first computed the statistics of each feature map and select the least adversarial based on these statistics for pruning. The experimental results show that increasing pruning ratio results in degradation of network performance. On Cifar-10 dataset, introducing 70% pruning in the network resulted in 50% performance degradation but on the other hand, the inference time is reduced by 60% of actual inference time. To compensate for the performance degradation, we retrained the pruned network. After retraining the network, we found that the accuracy is improved even for a high pruning ratio. The proposed techniques performed better than existing absolute weight sum voting technique