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

Abstract

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.

Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization

“…Furthermore, in terms of specificity, sensitivity, and F-score, the proposed model showcases competitive performance compared to the results presented in [26]. Notably, the utilization of PSO for optimizing model performance or feature selection, as explored in [27][28][29], also demonstrates the effectiveness of such an approach. The achieved accuracy, precision, recall, and AUC by the proposed model highlight its potential as a high-performance CNN architecture for AD vs. NC detection when leveraging PSO for hyper-parameter selection.…”

“…Compared to fuzzy C-Means and K-Means clustering algorithms, the PSO-based PIDC algorithm provides better segmentation of various brain subjects, demonstrating that 92% of segmentation accuracy was achieved with the PSO and the PIDC algorithms. While in paper [28], the PSO algorithm is used with the decision tree method, the PSO algorithm is used for the feature selection process, and according to the experiment's findings, the PSO-based random forest algorithm achieved a 93.56% accuracy rate to detect Alzheimer's disease.…”

Applications of ML and DL Algorithms in The Prediction, Diagnosis, and Prognosis of Alzheimer’s Disease

“…Leaves are called leaf nodes, and intermediate subsets are called internal nodes [28]. Because it does not suffer from overfitting, a random forest model outperforms a decision tree in terms of performance [29]. Random forest-based models are made up of different (DTs), each marginally different [29].…”

Early Detection of Alzheimer's Disease with the C4.5 Algorithm Based on BPSO (Binary Particle Swarm Optimization)

“…Berdasarkan pada penelitian yang dilakukan oleh Bari Antor di atas, algoritma Decision Tree tidak memiliki performa yang cukup baik. Akan tetapi, apabila algoritma Decision Tree dilakukan seleksi fitur dengan algoritma PSO, maka hal tersebut dapat meningkatkan performa dari algoritma Decision Tree dengan ditandai bertambahnya nilai akurasi yang dihasilkan [15]. Algoritma PSO merupakan algoritma optimasi yang efektif untuk memecahkan masalah pada algoritma Decision Tree.…”