# Dementia Classification Using Deep Reinforcement Learning for Early Diagnosis
## 2 Related Work
Adaptive-SMOTE is a method that enhances the SMOTE approach by selecting risk and internal data groups from the minority class and constructing a new minority class based on the selected data. This is done to prevent the categorization boundary from expanding and to preserve the distributional properties of the original data. By contrast, Gaussian Oversampling combines the Gaussian distribution with a reduction in the number of dimensions. This shortens the tail of the Gaussian distribution. [PERSON] et al. [29] analyzed three modern architectures: the VGG16, the ResNet-152, and the DenseNet-121. A total of 1315 participants were included. They utilized optimization techniques to train the neural network (NN) to deal with the limited data, including multiple dementia types. Their efforts resulted in a computerized and standardized evaluation of a person's dementia test result and severity level. The work exceeded both the declared state-of-the-art and human accuracies, with screening levels of 96.65% accuracy and score accuracies as high as 98.54 percent. [PERSON] et al. [30] analyzed MRI scans from 2146 patients (1,343 for verification and 803 for training). A deep learning algorithm was created and validated to predict the progression of MCI participants to Alzheimer's disease in a time-to-event analysis setting. [PERSON] et al. [31] employed deep learning to construct open-source software for the classification of dementia in MRI images. A framework for deep learning is used to develop a model capable of distinguishing between dementia patients and healthy individuals. The findings demonstrate that the proposed technique can identify individuals suspected of having dementia. [PERSON] et al. [32] summarized the current state-of-the-art machine-learning approaches for dementia in healthcare informatics. The research analyzes and evaluates current scientific methodologies to identify significant challenges and obstacles when dealing with vast quantities of health data. While machine learning has demonstrated promise in data analysis for dementia care, there have been few attempts to utilize advanced machine learning techniques to leverage unified heterogeneous data. [PERSON] et al. [33] presented a novel ML-based method for identifying and monitoring AD-like diseases. The DL analysis of MRI scans is used to diagnose AD-like disorders, together with an activity monitoring framework that records participants' everyday tasks using body-worn inertial sensors. The activity surveillance system provides a foundation for assisting patients with daily tasks and gauges their sensitivity based on their activity level. [PERSON] and [PERSON] [34] proposed a new supervised ML technique for diagnosing Alzheimer's disease using MRI. They used the OASIS and ADANI datasets. They obtained an image with improved contrast, applied Fuzzy C Mean clustering to segment tissues, and extracted features from segmented brain tissues. They used the Correlation-Based Feature Selection (CFS) techniques to reduce the dimensionality of features. Table 1 summarizes important contributions to the literature on DL-based dementia.