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Detecting Alzheimer’s Disease by The Decision Tree Methods Based On Particle Swarm Optimization

In this study aims to determine the classification of Alzheimer’s disease, this disease is a dangerous disease that can eliminate memory loss and can even result in a loss of ability to remember. For this reason, early detection of this disease is needed so that it can prepare for medical treatment. In this study the proposed method is to compare several decision tree methods with feature or attribute selection using the Particle Swarm Optimization (PSO) algorithm with the Alzheimer OASIS 2 dataset: Longitudinal Data from kaggle.com. The results of experiments with ten-fold cross validation, by testing the decision tree algorithm before the feature or attribute selection is performed, the highest accuracy value is obtained from the random forest algorithm with a value of 91.15%. The feature selection process is carried out using the PSO algorithm and the experiment is repeated using the Decision tree, the PSO-based random forest algorithm has the highest accuracy value of 93.56% with a kappa value of 0.884. Feature or attribute selection using the PSO algorithm is proven to be able to improve the accuracy of the decision tree algorithm, and is included in the algorithm with a very good range of values.


Introduction
Alzheimer's disease is the most common trigger for dementia that occurs in older people [1], this disease attacks the nervous system of the brain which can irreversibly trigger the loss of neuron cells resulting in decreased memory [2] which results in a loss of ability to remember, difficulty in communication, thinking clearly, changes in behavior and ability to take care of themselves [1]. Memory loss in patients with Alzheimer's occurs gradually which is experienced for three to nine years [2]. It is estimated that around 44 million people worldwide in 2015 have Alzheimer's disease or dementia [3]. This disease is recognized as a fatal disease that leads to death and is the main cause of death ranked 7 in high-income countries [4]. In Indonesia, Alzheimer's disease continues to increase from 1 million sufferers in 2013 to 1.2 million in 2015 and is predicted to continue to increase to 2.2 million by 2020. However, until now there has not been found a drug that can cure this disease [5]. Therefore, the importance of early detection to begin to plan for treatment and adequate medical needs as well as testing new drugs for the disease [6].
In diagnosing Alzheimer's disease, one method that can be used is to apply data mining classification methods. The classification method is included in the supervised learning method  [7]. Research on Alzheimer's disease using data mining classification methods has been widely carried out, such as research [8] comparing the DA, NN, SVM, DT, KNN and TANN algorithms, obtained the TANN method has a high performance of 98%. Research [9] uses an algorithmic approach to detect Alzheimer's disease from non-image data using an affiliation algorithm model with an accuracy of 87%. Research [10] applies the KNN algorithm with two tasks, namely the reduction of dimensions of the high dimensional feature vector and the classification that produces an adaptive class, and the KNN algorithm is proven to work well. Research [11] using three different feature extractions namely GMV, GLCM, Gabor and improvised by the SVM-RFE algorithm, proved that the algorithm was effective in improving classification performance. Research [2] using Fuzzy C-Mean for feature Segmentation, GLCM for feature extraction and SVM for classification, obtained 93.33% accuracy results.
Decision tree algorithm is the best algorithm in every decision making, where the decision tree algorithm has several models namely CHAID, C4.5, ID3 and Random Forest [12]. This algorithm has been widely used by researchers for the case of classification and prediction, but it has a weakness that often overlaps, especially in classes and criteria which are quite numerous, so it requires time and the amount of memory to increase [13] [14].
Particle Swarm Optimization (PSO) is an effective optimization algorithm that can solve problems in the decision tree algorithm [15], because PSO can optimize overlapping datasets and can select a large number of attribute features [16]. This study aims to predict Alzheimer's disease using the Alzheimer's OASIS 2 dataset: Longitudinal Data obtained from the Kaggle site and published by Jacob Boysen, by finding the best accuracy value of the PSO-based Decision Tree algorithm and testing using 10-fold cross-validation.
2. Methods 2.1. C4.5 Algorithm C4.5 algorithm is part of the decision tree algorithm group, this algorithm is one of the solutions to solving cases that are often used in solving classification model problems and including the 10 best most popular algorithms [17]. The C4.5 algorithm has two equations, the first calculates the entropy value and the second calculates the gain value, for the Gain value itself its function is to select attributes as a node, for the root node is chosen based on the highest Gain value of the existing attributes. Following the C4.5 algorithms are: Calculating the entropy value used by the formula: Note: S as the set of cases; A as a feature; n as the partition number attribute A; Si as the proportion of Si to S; and S as the number of cases in S.
Note: S as the set of cases; A as an attribute; n as the number of partitions S and pi as the proportion of Si to S.

ID3 (Iterative Dichotomy 3) Algorithm
Iterative Dichotomy 3 (ID3) is a tree classification algorithm invented by J. Ross Quinlan (1979), by utilizing Shanon's information theory or Information Theory [18]. Following is the equation Note: S as a dataset (dataset) of cases; k as many S partitions; pj as the probability obtained from Sum (Yes) divided by Total Cases. After getting the entropy value, the attribute selection is done by assessing the largest information gain.
Note: S as the sample data space used for training; A as an attribute; Si as the number of samples for the value of V; S as the sum of all data samples and Entropy (S) as entropy for samples that have a value of i.

CHAID (Chi-Square Automatic Interaction Detection) Algorithm
The CHAID algorithm is a classification tree technique that tests independent variables one by one, separating the categories in the variables used in the analysis phase, the arrangement is based on the level of statistical significance of chi-square of the dependent variable [19], three stages must be done in the analysis CHAID is a merger, separation, and termination. At the merge stage contingency tables are formed of the dependent variables and independent variables that have been categorized.
Note: 0 rk and e rk as the number of observations and expectation values in the ker row and k-column. The hypothesis testing criteria is to reject H0 if X 2 count> tabel 2 table = 0.05 with degree of freedom (k-1) (r-1).

Random Forest Algorithm
Random Forest is an ensemble learning method used to improve the accuracy of a data classification [20]. Increased accuracy is done through a combination of many sorters from a similar method and gets a final classification prediction through the voting process [21]. In Random Forest, many trees are used so that a forest or forest is formed and then each tree is analyzed continuously [22]. Equations for calculating the Gini Index in the classification, use the equation below [22]: Note: the value of pi is the probability of the object to be classified in a certain class/feature.

Particle swarm optimization (PSO) Algorithm
PSO is an optimization algorithm developed by Kennedy and Eberhart in 1995. This algorithm is inspired by the lifestyle of the movements of birds and fish. The advantage of the Particle Swarm Optimization method is that it has a simple concept, but is easy to implement, and is efficient in calculations than other mathematical algorithms [23]. Broadly speaking, Kennedy and Eberhart describe the PSO algorithm with the equation below: Note: Note: X i as candidate for particle completion; P i as experience of best particle position; g as information of best particle environment; X i (t-1) as current particle position; ϕ as random vector with score range of 0.1; C 1 as coefficient of cognitive learning and C 2 as coefficient of social learning.

Figure 1. The Proposed Methods
Based on the description of the method proposed in Figure 1, the flow of research can be explained through the following steps: 1 Starting from the selection of datasets that will be used in the research process, in this study using Alzheimer's disease dataset from https://www.oasis-brains.org/OASIS 2 The next step is to divide the dataset into 10 parts using 10-fold cross-validations, the distribution of the dataset will be training and testing data. 3 After dividing the dataset, then selection of features or attributes using the PSO method. 4 The next step after obtaining the best attributes, then testing is done using a comparative Decision Tree algorithm (C4.5, ID3, CHAID and Random Forest). 5 Meanwhile, the testing data whose attributes are selected are validated by comparing the Decision Tree algorithm 6 After the data transfer and data testing are validated using the Decision Tree comparison algorithm, the final step is the presentation of the results of the data classification comparison.

Result and Discussion
This stage explains the results of the experiment using an algorithm that is included in the decision tree with feature or attribute selection using PSO, following table 1 the results of the decision tree algorithm experiment using 10-fold cross-validation before using the feature or attribute selection: After that the feature or attribute selection is performed using PSO, the results of the feature or attribute selection that affect the classification of Alzheimer's disease, MMSE and nWBV attributes have a value of 0, this shows that these attributes do not affect the results of the classification, the results of the selection of features or attributes can be seen in table 2 below: The results of this feature or attribute selection will be retested using a decision tree classification algorithm using 10-fold cross-validation. The following table 3 experimental results from the PSO-based decision tree algorithm:

Conclusionst
The results of the comparison of decision tree algorithms using feature selection or PSO algorithm attributes, PSO-based random forest algorithm has the highest accuracy value of the other algorithms that is equal to 93.56% with a kappa value of 0.884, in other words that the PSO algorithm is able to improve the accuracy of the random forest algorithm, and all Decision tree algorithm has a kappa value which is categorized very good. This research can be developed by comparing the data classification method and optimizing feature selection or other attributes, so that it has better accuracy than the current research results.