Early detection of Alzheimer's disease using cognitive features: A voting-based ensemble machine learning approach

Early detection of Alzheime 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 accurac

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

Dual tension: Lassa fever and COVID-19 in Nigeria

Lassa fever is a viral hemorrhagic, zoonotic disease that is a continuous health issue in West African countries such as Sierra Leone, Liberia, Guinea and Nigeria [1]. Ingestion and inhalation are the most common ways of transmission of Lassa fever. Patients generally get infected due to an exposure to food or household items which are contaminated with urine or droppings of infected Mastomys rodents. Lassa fever was first discovered in Lassa town, Borno State in Nigeria. It causes around 1000,000 – 300,000 infections each year with approximately 5,000 deaths in Nigeria [1]. In 2020, 70 deaths have been reported in 26 Nigerian states and the Federal Capital Territory. Among all those cases, 75% were from Edo, Ondo, and Ebonyi [2]. As of May 2020, Nigeria has recorded 991 confirmed cases and 191 deaths with case fatality ratio of 19.3% [3]. To prevent and mitigate the negative impact of Lassa Fever, the Nigeria Center for Disease Control (NCDC) has formed an interdisciplinary, multi-partner technical team to control outbreaks in affected Nigerian states