AlzCoGame: A Serious Game using Machine Learning for the Early Diagnosis of Alzheimer's Disease (Preprint)

2020 ◽  
Author(s):  
Samiha Mezrar ◽  
Fatima Bendella
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Random Forest ◽  
Early Diagnosis ◽  
Serious Game ◽  
Machine Learning Techniques ◽  
Mini Games ◽  
Cognitive Problems

UNSTRUCTURED Older people can develop cognitive problems with a range of symptoms including memory, perception, and difficulty in solving problems known as Alzheimer's disease (AD). Early diagnosis of Mild Cognitive Impairment (MCI), which can lead to AD, plays an important role in the management of patients to slow the decline in cognitive function, as treatments are effective early in the course of the disease. For this, advanced computer technologies can provide a tool for early detection of AD and for predicting the progression of the disease. This article presents a serious game formed of 16 mini-games that aimed to detect AD or MCI at the mild stage, based on gamification techniques and machine learning (ML) by overcoming the traditional testing limitations. The gamified cognitive tool, named AlzCoGame, assesses the main cognitive domains considered to be the most relevant indicators for the cognitive impairment diagnosis: working memory, episodic memory, executive functions, visuospatial orientation, concentration, and attention. Six predictive ML models such as Random Forest (RF), Naïve Bayes (NB), Decision Tree (DT), Logistic Regression (LR), AdaBoost (AB), and Extra Tree (ET) have been implemented using the AlzCoGame dataset. To validate the model's performance, we used the K-fold cross-validation and classification metrics (accuracy, precision, specificity, sensitivity, F1-Score, and ROC curve (receiver operating characteristic)). The results obtained indicate that Random Forest classifier gave better performances with average Sensitivity = 0.89, Specificity = 0.93, Accuracy = 0.92, 1-Score = 0.91 and ROC = 0.91. We can conclude that the inclusion of machine learning techniques and serious games can be used to improve some aspects of the clinical diagnosis of cognitive impairment. Moreover, clinical trials are necessary to prove the effects of this gamified program on cognitive functions and to assess usability measures.

scholarly journals Efficient Machine Learning Techniques to Diagnose and Predict Alzheimer’s disease

2020 ◽  
Vol 9 (3) ◽  
pp. 3953-3960
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Mechanisms ◽  
Learning Techniques

Recent research in computational engineering have evidenced the design and development numerous intelligent models to analyze medical data and derive inferences related to early diagnosis and prediction of disease severity. In this context, prediction and diagnosis of fatal neurodegenerative diseases that comes under the class of dementia from medical image data is considered as the challenging area of research for many researchers. Recently Alzheimer’s disease is considered as major category of dementia that affects major population. Despite of the development of numerous machine learning models for early diagnosis of Alzheimer’s disease, it is observed that there is a lot more scope of research. Addressing the same, this article presents a systematic literature review of machine learning techniques developed for early diagnosis of Alzheimer’s disease. Furthermore this article includes major categories of machine learning algorithms that include artificial neural networks, Support vector machines and Deep learning based ensemble models that helps the budding researchers to explore the scope of research in predicting Alzheimer’s disease. Implementation results depict the comparative analysis of state of art machine learning mechanisms.

scholarly journals Alzheimer's Disease Early Detection Using Machine Learning Techniques

2021 ◽  
Author(s):  
Roobaea Alroobaea ◽  
Seifeddine Mechti ◽  
Mariem Haoues ◽  
Saeed Rubaiee ◽  
Anas Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Detection ◽  
Learning Techniques

Abstract Alzheimer's is the main reason for dementia, that affects frequently older adults. This disease is costly especially, in terms of treatment. In addition, Alzheimer's is one of the deaths causes in the old-age citizens. Early Alzheimer's detection helps medical staffs in this disease diagnosis, which will certainly decrease the risk of death. This made the early Alzheimer's disease detection a crucial problem in the healthcare industry. The objective of this research study is to introduce a computer-aided diagnosis system for Alzheimer's disease detection using machine learning techniques. We employed data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS) brain datasets. Common supervised machine learning techniques have been applied for automatic Alzheimer’s disease detection such as: logistic regression, support vector machine, random forest, linear discriminant analysis, etc. The best accuracy values provided by the machine learning classifiers are 99.43% and 99.10% given by respectively, logistic regression and support vector machine using ADNI dataset, whereas for the OASIS dataset, we obtained 84.33% and 83.92% given by respectively logistic regression and random forest.

scholarly journals Multi-Method Analysis of Medical Records and MRI Images for Early Diagnosis of Dementia and Alzheimer’s Disease Based on Deep Learning and Hybrid Methods

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2860
Author(s):  
Badiea Abdulkarem Mohammed ◽  
Ebrahim Mohammed Senan ◽  
Taha H. Rassem ◽  
Nasrin M. Makbol ◽  
Adwan Alownie Alanazi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Random Forest ◽  
Early Diagnosis ◽  
High Performance ◽  
Hybrid Methods ◽  
Machine Learning Algorithms ◽  
Hybrid Techniques

Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between neurons in the brain. So far, no effective medications have been discovered for dementia and Alzheimer’s disease. Thus, early diagnosis is necessary to avoid the development of these diseases. In this study, efficient machine learning algorithms were assessed to evaluate the Open Access Series of Imaging Studies (OASIS) dataset for dementia diagnosis. Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer’s disease. For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the low-dimensional space. All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia. The random forest algorithm achieved an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively. The second dataset, the MRI image dataset, was evaluated by AlexNet and ResNet-50 models and AlexNet+SVM and ResNet-50+SVM hybrid techniques. All models achieved high performance, but the performance of the hybrid methods between deep learning and machine learning was better than that of the deep learning models. The AlexNet+SVM hybrid model achieved accuracy, sensitivity, specificity and AUC scores of 94.8%, 93%, 97.75% and 99.70%, respectively.

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease

2020 ◽  
Vol 16 (1s) ◽  
pp. 1-35 ◽  
Author(s):  
M. Tanveer ◽  
B. Richhariya ◽  
R. U. Khan ◽  
A. H. Rashid ◽  
P. Khanna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Early diagnosis of mild cognitive impairment and Alzheimer's disease based on salivary lactoferrin

2017 ◽  
Vol 8 (1) ◽  
pp. 131-138 ◽  
Author(s):  
Eva Carro ◽  
Fernando Bartolomé ◽  
Félix Bermejo‐Pareja ◽  
Alberto Villarejo‐Galende ◽  
José Antonio Molina ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Diagnosis

Neuro-Imaging Machine Learning Techniques for Alzheimer's Disease Diagnosis

2021 ◽  
pp. 191-209
Author(s):  
Gehad Ismail Sayed ◽  
Aboul Ella Hassanien
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Bench Mark ◽  
Learning Tools ◽  
Diagnosis System ◽  
Neuro Imaging ◽  
Medical Imagining

Alzheimer's disease (AD) is considered one of the most common dementia's forms affecting senior's age staring from 65 and over. The standard method for identifying AD are usually based on behavioral, neuropsychological and cognitive tests and sometimes followed by a brain scan. Advanced medical imagining modalities such as MRI and pattern recognition techniques are became good tools for predicting AD. In this chapter, an automatic AD diagnosis system from MRI images based on using machine learning tools is proposed. A bench mark dataset is used to evaluate the performance of the proposed system. The adopted dataset consists of 20 patients for each diagnosis case including cognitive impairment, Alzheimer's disease and normal. Several evaluation measurements are used to evaluate the robustness of the proposed diagnosis system. The experimental results reveal the good performance of the proposed system.

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