An artificial neural network to classify healthy aging in elderly Brazilians (original) (raw)
Related papers
Background and Objectives: A huge number of solutions based on computational systems have been recently developed for the classification of cognitive abnormalities in older people, so that individuals at high risk of developing neurodegenerative diseases, such as Cognitive Impairment and Alzheimer?s disease, can be identified before the manifestation of the diseases. Several factors are related to these pathologies, making the diagnostic process a hard problem to solve. This paper proposes a computational model based on the artificial neural network to classify data patterns of older adults. Methods: The proposal takes into account the several parameters as diagnostic factors as gender, age, the level of education, study time, and scores from cognitive tests (Mini-Mental State Examination, Semantic Verbal Fluency Test, Clinical Dementia Rating and Ascertaining Dementia). This non-linear regression model is designed to classify healthy and pathological aging with machine learning techniques such as neural networks, random forest, SVM, and stochastic gradient boosting. We deployed a simple linear regression model for the sake of comparison. The primary objective is to use a regression model to analyze the data set aiming to check which parameters are necessary to achieve high accuracy in the diagnosis of neurodegenerative disorders. Results: The analysis demonstrated that the usage of cognitive tests produces median values for the accuracy greater than 90%. The ROC analysis shows that the best sensitivity performance is above 98% and specificity of 96% when the configurations have only cognitive tests. Conclusions: The presented approach is a valuable tool for identifying patients with dementia or MCI and for supporting the clinician in the diagnostic process, by providing an outstanding support decision tool in the diagnostics of neurodegenerative diseases.
An Artificial Neural Network Model for Assessing Frailty-Associated Factors in the Thai Population
International Journal of Environmental Research and Public Health
Frailty, one of the major public health problems in the elderly, can result from multiple etiologic factors including biological and physical changes in the body which contribute to the reduction in the function of multiple bodily systems. A diagnosis of frailty can be reached using a variety of frailty assessment tools. In this study, general characteristics and health data were assessed using modified versions of Fried’s Frailty Phenotype (mFFP) and the Frail Non-Disabled (FiND) questionnaire (mFiND) to construct a Self-Organizing Map (SOM). Trained data, composed of the component planes of each variable, were visualized using 2-dimentional hexagonal grid maps. The relationship between the variables and the final SOM was then investigated. The SOM model using the modified FiND questionnaire showed a correct classification rate (%CC) of about 66% rather than the model responded to mFFP models. The SOM Discrimination Index (SOMDI) identified cataracts/glaucoma, age, sex, stroke, pol...
Application of machine learning in measurement of ageing and geriatric diseases: A systematic review
Background As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning (ML) has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and application of machine learning methods in this area. Methods This systematic review followed PRISMA guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. Peer-reviewed articles were searched in the PubMed database with a focus on ML methods and the older population. Results A total of 59 papers were selected from the 81 identified pap...
Procedia Computer Science, 2016
A method of the evaluation of the risk of cardiovascular events in the group of middle-aged male workers was developed on the basis of artificial neural networks (ANN). The list of analyzed variables included parameters of allostatic load and signs of myocardial involvement. The results were compared with traditional scales and risk charts (SCORE, PROCAM, and Framingham). A better prognostic value of the proposed model was observed, which makes it reasonable to use both additional markers and ANN.
Recognition of patients with cardiovascular disease by artificial neural networks
Annals of Medicine, 2004
BACKGROUND. Arti®cial neural networks (ANNs) are BACKGROUND. Arti®cial neural networks (ANNs) are computer algorithms inspired by the highly interactive computer algorithms inspired by the highly interactive processing of the human brain. When exposed to complex processing of the human brain. When exposed to complex data sets, ANNs can learn the mechanisms that correlate data sets, ANNs can learn the mechanisms that correlate different variables and perform complex classi®cation tasks. different variables and perform complex classi®cation tasks. AIMS. A database, of 949 patients and 54 variables, was AIMS. A database, of 949 patients and 54 variables, was analysed to evaluate the capacity of ANNs to recognise analysed to evaluate the capacity of ANNs to recognise patients with ( patients with (VE 1 , n = = 196) or without ( 196) or without (VE VE 2 2 , n , n = = 753) a 753) a history of vascular events on the basis of vascular risk history of vascular events on the basis of vascular risk factors (VRFs), carotid ultrasound variables (UVs) or both. factors (VRFs), carotid ultrasound variables (UVs) or both. METHOD. The performance of ANN was assessed by calcu-METHOD. The performance of ANN was assessed by calculating the percentage of correct identi®cations of lating the percentage of correct identi®cations of VE 1 and and VE 2 patients (sensitivity and speci®city, respectively) and patients (sensitivity and speci®city, respectively) and the prediction accuracy (weighted mean between sensitivity the prediction accuracy (weighted mean between sensitivity and speci®city). and speci®city).
Artificial Neural Networks in the Recognition of Patients at High Risk of Cardiovascular Disease
2002
AIM: To investigate the role of artificial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients. METHODS: A dataset of 29 input variables of 253 atrophic body gastritis patients was applied to artificial neural networks (ANNs) using a data optimisation procedure (standard ANNs, T&T-IS protocol, TWIST protocol). The target variable was the presence of thyroid disease. RESULTS: Standard ANNs obtained a mean accuracy of 64.4% with a sensitivity of 69% and a specificity of 59.8% in recognizing atrophic body gastritis patients with thyroid disease. The optimization procedures (T&T-IS and TWIST protocol) improved the performance of the recognition task yielding a mean accuracy, sensitivity and specificity of 74.7% and 75.8%, 78.8% and 81.8%, and 70.5% and 69.9%, respectively. The increase of sensitivity of the TWIST protocol was statistically significant compared to T&T-IS. CONCLUSION: This study suggests that artificial neural networks may be taken into consideration as a potential clinical decision-support tool for identifying ABG patients at risk for harbouring an unknown thyroid disease and thus requiring diagnostic work-up of their thyroid status.
A Study on Classification Using Machine Learning for Dementia Evaluation
2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), 2020
Recently, the number of dementia patients has been increasing due to the aging society. In Japan, a paper-based examination is the mainstream to measure the cognitive function of a subject, but these paper-based tests give much burden to not only patients but also evaluators like facility and medical staff. Therefore, it is necessary to develop a system that can automatically judge the degree of dementia progression, not to burden the doctor. Also, it is required to add play ability not to be a burden on the elderly. From this point of view, the authors developed a recreation game like a puzzle game. This system is easy to play for elderly people and is not a burden. Also, the question-answer is clear, so it is suitable for automatic judgment. We use the obtained features during recreation game to diagnose the degree of dementia progression. We committed the capability of machine learning techniques. Finally, we discussed that the collected features are sufficient to diagnose the degree of dementia progression.
Quantifying Human Biological Age: A Machine Learning Approach
Quantifying human biological age is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age prediction, each with its advantages and limitations. In this work, we first introduce a new anthropometric measure (called Surface-based Body Shape Index, SBSI) that accounts for both body shape and body size, and evaluate its performance as a predictor of all-cause mortality. We analyzed data from the National Health and Human Nutrition Examination Survey (NHANES). Based on the analysis, we introduce a new body shape index constructed from four important anthropometric determinants of body shape and body size: body surface area (BSA), vertical trunk circumference (VTC), height (H) and waist circumference (WC). SBSI is generally linear with age, and increases with increasing mortality, when compared with other popular anthropometric indices of body shape. We then investigate whether human body shape can be exploited for reliable age estimation for adult humans. We introduce a new multi-stage approach, based on human body measurements. Specifically, we develop an eigen body shape model, and use this to perform body shape clustering. Each cluster contains individuals with similar body shapes as captured by the eigen body shape model. First, we perform initial age estimation based on the body shape model. This initial estimate is then used to assign the subject into a probable age group. The second stage of estimation is then performed by using a specific estimation model as determined by the age group and body shape model. We then apply information from the neighborhood context to further improve estimation accuracy and stability. Experimental results show that, with appropriate modeling, human body shape can be used in human age estimation. We obtain a mean absolute error (MAE) of 5.90 years on the NHANES dataset, using 10-fold cross-validation. We then study the question of whether blood biomarkers can be used for reliable biological age estimation. We propose a new biological age estimation method, and investigate the performance of the new method against popular biological age estimation methods. We introduce a centroid based approach, using the notion of age neighborhoods. Specifically, we develop a model, based on which we compute biological age using blood biomarkers. Compared with current popular methods for biological age prediction, our results show that, the proposed age neighborhood model is robust, and results in improved performance in human biological age prediction. Furthermore, we investigate whether human locomotor activity can be exploited for biological age estimation for adult humans. We introduce an approach based on deep convolutional long short term memory (ConvLSTM) to predict biological age, using human physical activity as recorded by a wearable device. We consider five deep biological age estimation models including the proposed approach and compare their performance on the NHANES physical activity dataset. Results on mortality hazard prediction using both the Cox proportionality hazard model and Kaplan-Meier curves each show that the proposed method for estimating biological age outperforms other state-of-the-art approaches. This work has significant implications in various fields, such as health assessment, forensic science, biometrics, security, and in vaccination and immunization when the true age of the subject is unknown. Our work also has implications in combining wearable sensors and deep learning techniques for improved health monitoring, for instance, in a mobile health environment. To my wonderful parents, sister, and wife iii List of Figures ix List of Tables xi Bibliography 133 viii 7.1 Ethnic variation results of the Cox proportional hazard (Cox PH) model and Logrank test.
… Data Engineering and …, 2007
This work tries to go a step further in the development of methods based on automatic learning techniques to parse and interpret data relating to cognitive decline (CD). There have been studied the neuropsychological tests of 267 consultations made over 30 patients by the Alzheimer's Patient Association of Gran Canaria in 2005. The Sanger neural network adaptation for missing values treatment has allowed making a Principal Components Analysis (PCA) on the successfully obtained data. The results show that the first three obtained principal components are able to extract information relating to functional, cognitive and instrumental sintomatology, respectively, from the test. By means of these techniques, it is possible to develop tools that allow physicians to quantify, view and make a better pursuit of the sintomatology associated to the cognitive decline processes, contributing to a better knowledge of these ones.
2012
In this paper, we report a comparison study of 7 non parametric classifiers (Multilayer perceptron Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification trees and Random Forests) as compared to Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression tested in a real data application of mild cognitive impaired elderly patients conversion to dementia. When classification results are compared both on overall accuracy, specificity and sensitivity, Linear Discriminant Analysis and Random Forests rank first among all the classifiers.