Comprehensive Performance Analysis of Neurodegenerative disease Incidence in the Females of 60-96 year Age Group

Neurodegenerative diseases such as Alzheimer's disease and dementia are gradually becoming more prevalent chronic diseases, characterized by the decline in cognitive and behavioral symptoms. Machine learning is revolu-tionising almost all domains of our life, including the clinical system. The application of machine learning has the potential to enormously augment the reach of neurodegenerative care thus building it more proficient. Throughout the globe, there is a massive burden of Alzheimer's and demen-tia cases; which denotes an exclusive set of difficulties. This provides us with an exceptional opportunity in terms of the impending convenience of data. Harnessing this data using machine learning tools and techniques, can put scientists and physicians in the lead research position in this area. The ob-jective of this study was to develop an efficient prognostic ML model with high-performance metrics to better identify female candidate subjects at risk of having Alzheimer's disease and dementia. The study was based on two diverse datasets. The results have been discussed employing seven perfor-mance evaluation measures i.e. accuracy, precision, recall, F-measure, Re-ceiver Operating Characteristic (ROC) area, Kappa statistic, and Root Mean Squared Error (RMSE). Also, a comprehensive performance analysis has been carried out later in the study

Multiresolution wavelet analysis of event-related EEG potentials using ensemble of classifier data fusion techniques for early diagnosis of Alzheimer\u27s disease

The recent advances and knowledge in medicine and nutrition have greatly improved our average life expectancy. An unfortunate consequence of this longer life span, however, is a dramatic increase in the number of individuals suffering from dementia, and more specifically, from Alzheimer\u27s disease (AD). Furthermore, AD remains under-diagnosed and under-treated until its more severe stages due to lack of standard diagnostic tools available to community clinics. A search for biomarkers that will allow early diagnosis of the disease is therefore necessary to develop effective medical treatments. Such a biomarker should be non-invasive, simple to obtain, safe, inexpensive, accurate, and most importantly, must be made available to local health clinics for maximum effectiveness. Event related potentials (ERPs) of the electroencephalogram have the potential to become such a diagnostic biomarker for AD. This work investigates the use of ERP signals for the early detection of AD. The analysis of the ERP signals is accomplished through multiresolution wavelet decomposition, producing time-frequency features in successive spectral bands. In previous studies, these feature sets were concatenated and used as inputs to a neural network classifier. This contribution investigates training an ensemble of classifiers on each feature set separately, and combining the ensemble decisions in a data fusion setting. Comparisons of intra-signal and inter-signal ensemble combinations are presented in along with the benefits of using an ensemble of classifiers in data fusion

Alzheimers Disease Diagnosis by Deep Learning Using MRI-Based Approaches

The most frequent kind of dementia of the nervous system, Alzheimer's disease, weakens several brain processes (such as memory) and eventually results in death. The clinical study uses magnetic resonance imaging to diagnose AD. Deep learning algorithms are capable of pattern recognition and feature extraction from the inputted raw data. As early diagnosis and stage detection are the most crucial elements in enhancing patient care and treatment outcomes, deep learning algorithms for MRI images have recently allowed for diagnosing a medical condition at the beginning stage and identifying particular symptoms of Alzheimer's disease. As a result, we aimed to analyze five specific studies focused on AD diagnosis using MRI-based deep learning algorithms between 2021 and 2023 in this study. To completely illustrate the differences between these techniques and comprehend how deep learning algorithms function, we attempted to explore selected approaches in depth

Machine Learning for Detection of Cognitive Impairment

The detection of cognitive problems, especially in the early stages, is critical and the method by which it is diagnosed is manual and depends on one or more specialist doctors, to diagnose it as the cognitive decline escalates into the early stage of dementia, e.g., Alzheimer's disease (AD). The early stages of AD are very similar to Mild Cognitive Impairment (MCI); it is essential to identify the possible factors associated with the disease. This research aims to demonstrate that automated models can differentiate and classify MCI and AD in the early stages. The present research used a combination of Machine Learning (ML) algorithms to identify AD, using gene expressions. The algorithms used for the classification of cognitive problems and healthy people (control) were: Linear Regression, Decision Trees (DT), Naîve Bayes (NB) and Deep Learning (DP). The result of this research shows ML algorithms can identify AD, in early stages, with an 80% accuracy, using a Deep Learning (DL) algorithm.Fil: Diaz, Valeria. Universidad de Palermo. Facultad de Ingeniería; ArgentinaFil: Rodríguez, Guillermo Horacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Early Detection of Alzheimer's Disease with Blood Plasma Proteins using Support Vector Machines

The successful development of amyloid-based biomarkers and tests for Alzheimer's disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities, we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease, suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers

Diagnosis of Parkinson's Disease Based on Voice Signals Using SHAP and Hard Voting Ensemble Method

Background and Objective: Parkinson's disease (PD) is the second most common progressive neurological condition after Alzheimer's, characterized by motor and non-motor symptoms. Developing a method to diagnose the condition in its beginning phases is essential because of the significant number of individuals afflicting with this illness. PD is typically identified using motor symptoms or other Neuroimaging techniques, such as DATSCAN and SPECT. These methods are expensive, time-consuming, and unavailable to the general public; furthermore, they are not very accurate. These constraints encouraged us to develop a novel technique using SHAP and Hard Voting Ensemble Method based on voice signals. Methods: In this article, we used Pearson Correlation Coefficients to understand the relationship between input features and the output, and finally, input features with high correlation were selected. These selected features were classified by the Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Gradient Boosting, and Bagging. Moreover, the Hard Voting Ensemble Method was determined based on the performance of the four classifiers. At the final stage, we proposed Shapley Additive exPlanations (SHAP) to rank the features according to their significance in diagnosing Parkinson's disease. Results and Conclusion: The proposed method achieved 85.42% accuracy, 84.94% F1-score, 86.77% precision, 87.62% specificity, and 83.20% sensitivity. The study's findings demonstrated that the proposed method outperformed state-of-the-art approaches and can assist physicians in diagnosing Parkinson's cases

Predictive analytics applied to Alzheimer’s disease : a data visualisation framework for understanding current research and future challenges

Dissertation as a partial requirement for obtaining a master’s degree in information management, with a specialisation in Business Intelligence and Knowledge Management.Big Data is, nowadays, regarded as a tool for improving the healthcare sector in many areas, such as in its economic side, by trying to search for operational efficiency gaps, and in personalised treatment, by selecting the best drug for the patient, for instance. Data science can play a key role in identifying diseases in an early stage, or even when there are no signs of it, track its progress, quickly identify the efficacy of treatments and suggest alternative ones. Therefore, the prevention side of healthcare can be enhanced with the usage of state-of-the-art predictive big data analytics and machine learning methods, integrating the available, complex, heterogeneous, yet sparse, data from multiple sources, towards a better disease and pathology patterns identification. It can be applied for the diagnostic challenging neurodegenerative disorders; the identification of the patterns that trigger those disorders can make possible to identify more risk factors, biomarkers, in every human being. With that, we can improve the effectiveness of the medical interventions, helping people to stay healthy and active for a longer period. In this work, a review of the state of science about predictive big data analytics is done, concerning its application to Alzheimer’s Disease early diagnosis. It is done by searching and summarising the scientific articles published in respectable online sources, putting together all the information that is spread out in the world wide web, with the goal of enhancing knowledge management and collaboration practices about the topic. Furthermore, an interactive data visualisation tool to better manage and identify the scientific articles is develop, delivering, in this way, a holistic visual overview of the developments done in the important field of Alzheimer’s Disease diagnosis.Big Data é hoje considerada uma ferramenta para melhorar o sector da saúde em muitas áreas, tais como na sua vertente mais económica, tentando encontrar lacunas de eficiência operacional, e no tratamento personalizado, selecionando o melhor medicamento para o paciente, por exemplo. A ciência de dados pode desempenhar um papel fundamental na identificação de doenças em um estágio inicial, ou mesmo quando não há sinais dela, acompanhar o seu progresso, identificar rapidamente a eficácia dos tratamentos indicados ao paciente e sugerir alternativas. Portanto, o lado preventivo dos cuidados de saúde pode ser bastante melhorado com o uso de métodos avançados de análise preditiva com big data e de machine learning, integrando os dados disponíveis, geralmente complexos, heterogéneos e esparsos provenientes de múltiplas fontes, para uma melhor identificação de padrões patológicos e da doença. Estes métodos podem ser aplicados nas doenças neurodegenerativas que ainda são um grande desafio no seu diagnóstico; a identificação dos padrões que desencadeiam esses distúrbios pode possibilitar a identificação de mais fatores de risco, biomarcadores, em todo e qualquer ser humano. Com isso, podemos melhorar a eficácia das intervenções médicas, ajudando as pessoas a permanecerem saudáveis e ativas por um período mais longo. Neste trabalho, é feita uma revisão do estado da arte sobre a análise preditiva com big data, no que diz respeito à sua aplicação ao diagnóstico precoce da Doença de Alzheimer. Isto foi realizado através da pesquisa exaustiva e resumo de um grande número de artigos científicos publicados em fontes online de referência na área, reunindo a informação que está amplamente espalhada na world wide web, com o objetivo de aprimorar a gestão do conhecimento e as práticas de colaboração sobre o tema. Além disso, uma ferramenta interativa de visualização de dados para melhor gerir e identificar os artigos científicos foi desenvolvida, fornecendo, desta forma, uma visão holística dos avanços científico feitos no importante campo do diagnóstico da Doença de Alzheimer