Alzheimer's Disease (AD) is a progressive neurological condition that leads to short-term memory loss, paranoia, and delusional ideas that are mistaken for the effects of stress or aging. In the United States, this Disease affects about 5.1 million people. AD does not have proper medical treatment. In order to control AD, continuous medication is necessary. AD (1) is chronic so that it can last for years or the rest of your life. Therefore, it is most important to prescribe medication at the appropriate stage so that the brain is not damaged to a great extent. Early detection of this Disease is a tedious and costly process since we must collect a lot of data and use sophisticated tools for prediction and have an experienced doctor involved. Automated systems are more accurate than human assessment and can be used in medical decision support systems because they are not subject to human errors. Based on previous research on AD, researchers have applied images (MRI scans), biomarkers (chemicals, blood flow), and numerical data extracted from the MRI scans to study this Disease. As such, they were able to determine whether a person was demented or not. In addition to shortening diagnosis time, more human interaction will be reduced by automating Alzheimer's diagnosis. In addition, automation reduces overall costs and provides more accurate results. For example, we can predict whether a patient is demented by analyzing MRI scans and applying prediction techniques. If a person has early-stage Alzheimer's Disease, they are considered demented. By doing so, we can achieve better accuracy.

When a person has Alzheimer's Disease in the early stages, they can usually function without any assistance. In some cases, the person can still work, drive, and partake in social activities. Although this is the case, the person may still feel uneasy or suffer from memory loss, such as not remembering familiar words and locations. People close to the individual notice that they have difficulty remembering their names. By conducting a detailed medical interview, a doctor may identify problems with memory and concentration in the patient. Common challenges in early stage of Alzheimer's Disease include,

Alzheimer's symptoms become more persistent as the Disease progresses. When people suffer from dementia, their ability to communicate, adapt to their environment, and eventually move is lost. It becomes much more difficult for them to communicate pain through words or phrases. Individuals may need substantial assistance with daily activities as their memory, and cognitive skills continue to decline. At this stage, individuals may:

Alzheimer's Disease is predicted using ML algorithms by using a feature selection and extraction technique, and the classification is conducted based on the oasis longitudinal dataset. The different techniques (2) involved in analyzing brain images for diagnosing diseases of the brain to provide a brief overview. Several major issues are discussed in this article relating to machine learning and deep learning-based brain disease diagnostics based on the results of the reviewed articles. The most accurate method of detecting brain disorders was found in this study and can be used to improve future techniques. Using machine learning and deep learning platforms, this study aims to combine recent research on four brain diseases: Alzheimer's disease, brain tumors, epilepsy, and Parkinson's disease. By using 22 brain disease databases that are used most during the reviews, the authors can determine the most accurate diagnostic method.

Martinez-Murcia et al. (3) uses deep convolutional autoencoders to explore data analysis of AD. Data-driven decomposition of MRI images allows us to extract MRI features that represent an individual's cognitive symptoms as well as the underlying neurodegeneration process. A regression and classification analysis are then performed to examine the distribution of the extracted features in a wide variety of combinations, and the influence of each coordinate of the autoencoder manifold on the brain is calculated. MMSE or ADAS11 scores, along with imaging-derived markers, can be used for over 80% accuracy to predict AD diagnosis.

A deep neural network is used with layers (4, 5)) that are all connected to perform binary classification. Each hidden layer is activated by a different activation function. A model with the best performance is chosen after k-folds validation Researchers at the Lancet Commission found that about 35% of Alzheimer's risk factors can be modified. The following factors can contribute to these risks: a lack of education, hypertension, obesity, hearing loss, depression, diabetes, lack of physical activity, smoking, and social isolation. Regardless of the impact of these factors at any stage of life, it is beneficial to eliminate them. Studies have suggested (6) that early intervention and treatment of modifiable Alzheimer's risk factors can prevent or delay 30% of cases of Alzheimer's (7). According to the Innovative Midlife Intervention for Alzheimer's Deterrence (In-MINDD) project (8) one way to calculate Alzheimer's risk based on risk factors is by using the Lifestyle for Brain Health (LIBRA) index (9–12). According to the National Academy of Medicine (13, 14) cognitive training, hypertension management, and increased physical activity were the three main categories of dementia intervention. The most common type of Alzheimer's is Alzheimer's Disease (AD). Among the types of Alzheimer's, Vascular Alzheimer's (VaD) is the second most common, followed by Alzheimer's with Lewy bodies. A few other types of Alzheimer's are associated with brain injuries, infections, and alcohol abuse. Tatiq and Barber (15) in their study suggested that Alzheimer's can be prevented by targeting modifiable vascular risk factors because these two types often co-exist in the brain and share some modifiable risk factors. Williams et al. (16) obtained predictions of cognitive functioning based on neuropsychological and demographic data using four different models: SVM, Decision Tree, NN, and Naïve-Bayes. In this case, average values were substituted for the missing values; the accuracy of Naive Bayes was the highest. Data from ADNI study are applied using ten-fold cross-validation (17, 18) and show high correlation between genetic, imaging, biomarker, and neuropsychological outcomes. MRI images from the OASIS dataset (19, 20) are analyzed using voxel-based morphometry. Table 1 summarizes the recent work on prediction of Alzheimer's disease.

The proposed approach consists of three basic steps. Firstly, the Alzheimer's disease dataset (24–26) was loaded into pandas for data preprocessing. This study utilized a longitudinal dataset, so a timeline of the study was necessary to gain further insight into the data. Our first step was to determine how cross-sectional the data appear to be, if either at a baseline or at a particular time. A complete analysis of the data was then conducted, including a comparison of the main study parts and the corresponding data collected during each visit. In this work, longitudinal MRI data is our primary data source. MRI data from 150 patients aged from 60 to 96 were included in the study. We scanned each patient at least once. Everyone is right-handed. Throughout the study, 72 of the patients were classified as “non-demented”. At the time of their initial visits, 64 patients were classified as being “Demented,” and they remained in this category throughout the study. Table 2 shows the dataset description of MRI data.

The Machine Learning techniques (26, 27) were applied to Alzheimer's disease datasets to bring a new dimension to predict Disease at an early stage. The raw Alzheimer's disease datasets are inconsistent and redundant, which affects the accuracy of algorithms (28, 29). Before evaluating machine-learning algorithms, data must be effectively prepared for analysis by removing unwanted attributes, missing values, and redundant records. Building a machine-learning model requires splitting the data into training and testing sets. In the following data preparation step, the training data were used to create a model, which was then applied to test data to predict Alzheimer's Disease (28, 30, 31). The model was trained from training set data, and test set data were used to test unseen data. Cross-validation was carried out by dividing the dataset into three subsets. Model predictions are made using one subset of the data (test data) and model performance is evaluated using the other subsets (training and validation) of the data. The data had been preprocessed, and we randomly divided it into an 80:20 ratio, with 80% going to training and 20% gone to testing. Figure 1 describes the system workflow for predicting the Alzheimer's Disease at early stage (32, 33).

We evaluate various performance metrics like accuracy, precision, recall and F1 score. To determine the best parameters for each model, we perform 5-fold cross-validation: Decision Tree, SVM, Random Forests, XGBoost and Voting. Finally, we compare accuracy of each model. Several metrics and techniques were used to identify overfitting and parameter tuning issues after the models were developed. Performance evaluations can either be binary or multiclass and are described using the confusion matrix. A learning model was developed to distinguish true Alzheimer's disease affected people from a given population and a novel Machine Learning classifier was developed and validated to predict and separate true Alzheimer's disease affected people. The following evaluation measures were calculated using these components: precision, recall, accuracy, and F-score. Based on this study, recall (sensitivity) is the proportion of people accurately classified as having Alzheimer's. The precision of Alzheimer's diagnosis is the rate of people correctly classified as not having the disease. Alternatively, F1 represents the weighted average of recall and precision, while accuracy represents the proportion of people correctly classified. According to the results, the patient receives a report that tells him or her what stage of Alzheimer's Disease he or she is currently in. It is very important to detect the stages because the stages are based on the responses of the patients. In addition, knowing the stage helps doctors better understand how the Disease is affecting them. This research used these environments, tools, and libraries to conduct its experiments and analysis:

The Figure 3 indicates that men are more likely than women to have dementia. Figure 4 that the non-demented group had much higher MMSE (Mini-Mental State Examination) scores than those with dementia.

The Figures 5A–C shows the analyzed value of ASF, eTIV and nWBV for Demented and Non-demented group of people. As indicated by the graph in Figure 5, the Non-demented group has a higher brain volume ratio than the Demented group. The reason for this is that the diseases influence the brain tissues causing them to shrink. Figure 6 shows the analyzed results of EDUC for Demented and Non-demented people.

Figure 7 shows the analysis on age attribute to find the percentage of people affected based on the demented and non-demented group. It is observed that a higher percentage of Demented patients are 70-80 years old than non-demented patients. It is likely that people with that kind of Disease have a low survival rate. Only a few people are over 90 years old.

From the above all analysis on the attributes, the following are the summary on intermediate results.

Table 4 shows the performance comparison of accuracy, precision, recall, and F1 score for different ML models. The performance measures are defined as,

Accuracy: It is the measure of finding the proportion of correctly classified result from the total instances.

Precision: This measures the number of correctly predicted positive rate divided by the total predicted positive rates. If the Precision value is 1, it is meant as a good classifier.

Recall: Recall is a true positive rate. If the recall is 1, it is meant as a good classifier.

F1 Score: It is a measure which considers both Recall and Precision parameters. F1 score becomes 1 only when both the measure such as Recall and Precision is 1.

The most common metrics are the conversions of the True Positive (TP), the False Positive (FP), the True Negative (TN), and the False Negative (FN) metrics. Figures 8–13 shows the confusion matrix for Decision tree, Random Forest, SVM, XG boost, Soft, and Hard Voting classifier ML models.

A comparison of training and testing accuracy has been conducted for each model to eliminate overfitting. For each model, precision, recall, accuracy, and F1-score are shown in Table 3. Based on the analysis showed in the Table 3, the results approved that the best and ideal techniques, which have a good performance, are random forest, and XGBoost. The accuracy value of Voting classifier model is also closer to the random forest, and XGBoost models. All the experimental results (the average accuracy, precision, recall, and F measure of each model) were collected for extra analysis. The comparative analyses among all the Machine Learning models in terms of accuracy, precision, recall, and F1 score are presented graphically in Figures 14–17 respectively.

Alzheimer's is a major health concern, and rather than offering a cure, it is more important to reduce risk, provide early intervention, and diagnose symptoms early and accurately. As seen in the literature survey there have been a lot of efforts made to detect Alzheimer's Disease with different machine learning algorithms and micro-simulation methods; however, it remains a challenging task to identify relevant attributes that can detect Alzheimer's very early. The future work will focus on the extraction and analysis of new features that will be more likely to aid in the detection of Alzheimer's Disease, and on eliminating redundant and irrelevant features from existing feature sets to improve the accuracy of detection techniques. By adding metrics like MMSE and Education to our model, we'll be able to train it to distinguish between healthy adults and those with Alzheimer's.

Publicly available datasets were analyzed in this study. This data can be found at: https://www.kaggle.com/jboysen/mri-and-alzheimers?select=oasis_cross-sectional.csv.

CK: research concept and methodology and writing—original draft preparation. VM: review and editing. SS: supervision. OK and CT: validation. All authors contributed to the article and approved the submitted version.

This research has been funded by Dirección General de Investigaciones of Universidad Santiago de Cali under call No. 01-2021.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.