Distinguishing age-related cognitive decline from dementias: A study based on machine learning algorithms (original) (raw)
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Diagnosis of Alzheimer's disease employing Neuropsychological and classification techniques
IEEE, 2015
All over the world, a large number of people are suffering from brain related diseases. Diagnosing of these diseases is the requirement of the day. Dementia is one such brain related disease. This causes loss of cognitive functions such as reasoning, memory and other mental abilities which may be due to trauma or normal ageing. Alzheimer's disease is one of the types of the dementia which accounts to 60-80% of mental disorders [1]. For the diagnosis of such diseases many tests are conducted. In this paper, the authors have collected the data of 466 subjects by conducting neuropsychological test. The subjects are classified as demented or not using machine learning techniques. The authors have preprocessed the data. The data set is classified using Naïve Bayes, Jrip and Random Forest. The data set is evaluated using explorer, knowledge flow and API. WEKA tool is used for the analysis purpose. Results show Jrip and Random forest performs better compared to Naïve Bayes.
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.
Classification and Investigation of Alzheimer Disease Using Machine Learning Algorithms
Biochemical and Biophysical Research Communications, 2020
Dementia is a globally identified problem . The occurrence of dementia increases abruptly with growing age .It is an irreversible brain disease which causes degeneration in the cognitive ability of a person affecting his thinking, memory and judgment. Throughout the world around 50 million people have dementia and around 10 million new cases are diagnosed every year. Hence addressing this issue has become need of the hour and early diagnosis of dementia is essential for the progress of more prevailing treatments. Early diagnosis of this disease is done using cognitive tests to determine the mental ability of a person. Some of the cognitive tests include CDR, MMSE, and Adden Brooke’s cognitive examination. In present research work using machine learning techniques we have tried to detect the dementia in early stage .The data composed for investigation consists of the gender, age education ,MMSE,CDR,ASF,Handedness,number of visits of the patient to the hospital who are clustered as de...
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.
Comparative Analysis of Various Machine Learning Algorithms for Detecting Dementia
Procedia Computer Science, 2018
Nowadays, there has been recent interest in applying machine learning techniques to the neurodegenerative disorders. Dementia is one such emerging global health issue and its early detection can be very helpful. A comparative analysis of four machine learning algorithms i.e., J48, Naïve Bayes, Random Forest and Multilayer Perceptron is presented in this paper. For the attribute reduction, the CFSSubsetEval is used. It has been shown that J48 is performing best among all the algorithms for the detection of Dementia.
Classification of Dementia Types from Cognitive Profiles Data
Lecture Notes in Computer Science, 2006
The Cognitive Drug Research (CDR) system is specifically validated for dementia assessment; it consists of a series of computerized tests, which assess the cognitive faculties of the patient to derive a cognitive profile. We use six different classification algorithms to classify clinically diagnosed diseases from their cognitive profiles. Good accuracy was obtained in separating patients affected by Parkinson's disease from demented patients, and in discriminating between Alzheimer's disease and Vascular Dementia. However, in discriminating between Parkinson disease with dementia (PDD) and dementia with Lewy bodies (DLB), the accuracy was only slightly superior to chance; the existence of a significant difference in the cognitive profiles of DLB and PDD is indeed questioned in the medical literature.
Early detection of dementia using Machine Learning techniques
Journal of IMS Group, 2022
Today, as many as 7% of adults aged sixty and above suffer from dementia. Over four million individuals in India suffer from dementia in some form. Dementia affects at least 44 million people worldwide, making it a global health concern that must be tackled. Dementia is a condition of the mind rather than a disease. It is defined as a significant deterioration in mental function from a prior higher level that interferes with a person's everyday activities. Abnormal brain alterations create the disorders included under the umbrella term "dementia." This condition causes a deterioration in thinking abilities, also known as cognitive capacities, that is severe enough to interfere with everyday living and independence. It also has an impact on one's conduct, emotions, and relationships. Early identification and diagnosis of dementia can help in halting disease progression and minimize stress and morbidity in patients and caregivers. The objective of this work is to implement different machine learning-based models to identify dementia using gathered data and brain MRI scans in general practice. The approach might be beneficial for detecting persons who may have dementia but have not been formally diagnosed or who have a tendency for it. To improve the results, appropriate feature engineering and data preparation were used. Finally, using suitable performance assessment parameters, the outcomes from all of the models were compared.
Psychiatry Research, 2019
Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders.
International Journal of Bioinformatics Research, 2010
There has been a steady rise in the number of patients suffering from Alzheimer's disease (AD) all over the world. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. The patient's records are collected from National Institute on Aging, USA. The Sample consisted of initial visits of 496 subjects seen either as control or as patients. Patients were concerned about their memory at the National Institute on Aging. It also consisted of patients and caregiver interviews. This research work presents different models for the classification of different stages of Alzheimer's disease using various machine learning methods such as Neural Networks, Multilayer Perceptron, Bagging, Decision tree, CANFIS and Genetic algorithms. The classification accuracy for CANFIS was found to be 99.55% which was found to be better when compared to other classification methods. Based on the outcome of classification accuracies, various management and treatment strategies such as pharmacotherapeutic and non pharmacotherapeutic interventions for mild, moderate and severe AD were elucidated, which can be of enormous use for the medical professionals in diagnosis and treatment of AD.
IEEE, 2015
Now a day's most of the people suffer from brain related neurodegenerative disorders. These disorders lead to various diseases. Dementia is one such disease. Dementia is a general term for a decline in mental ability severe enough to interfere with daily life. Alzheimer's disease is the most common type of dementia. Alzheimer's disease is one of the types of the dementia which accounts to 60-80% of mental disorders [1]. Diagnosis of the disease at the earlier stage is a crucial task. Diagnosis of the disease at the early stage will enable the diseased to have quality life. Authors have collected data from various neuropsychologists which consist of 250 patient's records. In this paper the authors focus on diagnosis of disease using various machine learning techniques of data mining. Authors have compared various classification techniques such as Naive Bayes, Decision tree algorithm J48, Random forest, JRIP and suggest Naïve bayes as the best technique.