Abstract
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.
Highlights
Licensee MDPI, Basel, Switzerland.The number of dangerous diseases has increased in recent years due to demographic shifts in developing and developed countries [1]
We displayed the results of dementia cases diagnosis, noting that the random forest classifier achieved better results than the rest of the classifiers, as it reached an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively
For the decision tree algorithm, it achieved an overall accuracy of 94% and precision, recall and F1 scores of 95%, 93% and 94%, respectively
Summary
Licensee MDPI, Basel, Switzerland.The number of dangerous diseases has increased in recent years due to demographic shifts in developing and developed countries [1]. Despite advances in medical techniques, effective treatments for dementia and Alzheimer’s disease remain elusive, except for some drugs that delay the diseases’ progression. Early diagnosis plays an important role in stopping the progression of the diseases to their advanced stages [1,2]. Of mental health are dementia and Alzheimer’s diseases because of their widespread prevalence among the elderly and their harmful effects on the elderly’s cognitive abilities to conduct daily activities normally. Dementia is the loss or impairment of memory to conduct healthy mental abilities due to age or disease; it is characterised by changes in the mind and behavioural disturbance or stroke. It is a syndrome that includes impaired memory, behaviour and thinking and the loss of ability to perform daily activities [3,4]
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