No abstract

Alzheimer is an irreversible neurological disorder. It impairs the memory and thinking ability of a person. Its symptoms are not known at an early stage due to which a person is deprived of receiving medication at an early stage. Dementia, a general form of Alzheimer, is difficult to diagnose and hence a proper system for detection of Alzheimer is needed. Various studies have been done for accurate classification of patients with or without Alzheimer’s disease (AD). However, accuracy of prediction is still a challenge depending on the type of data used for diagnosis. Timely identification of true positives and false negatives are critical to the diagnosis. This work focuses on extraction of optimal features using nature-inspired algorithms to enhance the accuracy of classification models. This work proposes two hybrid nature-inspired algorithms — particle swarm optimization with genetic algorithm (PSO_GA) and whale optimization algorithm with genetic algorithm, (WOA_GA) to improve prediction accuracy. The performance of proposed algorithms is evaluated with respect to various existing algorithms on the basis of accuracy and time taken. Experimental results depict that there is trade-off in time and accuracy. Results revealed that the best accuracy is achieved by PSO_GA while it takes higher time than WOA and WOA_GA. Overall WOA_GA gives better performance accuracy when compared to a majority of the compared algorithms using support vector machine (SVM) and AdaSVM classifiers.

No abstract

No abstract

One of the major concerns of biomedical datasets is high dimensionality. These dimensions may include irrelevant and redundant features that adversely affect the performance of classification algorithms. Extensive research has been done in the area of machine learning to handle high dimensionality. In literature, feature selection algorithms have been developed for this purpose. In this paper, a hybrid nature-inspired algorithm is proposed which is a combination of whale optimization algorithm and genetic algorithm for feature selection. The proposed algorithm is applied to four microarray datasets and one DNA sequence dataset and compared with classical feature selection methods. In all the algorithms decision tree classifier is mainly employed. To reach an approximate best solution and remove local solutions, the exploitation and exploration phases are balanced efficiently in the proposed algorithm. The convergence speed in the proposed algorithm is accelerated by the adaptive mechanisms. Overall results employ better performance on the majority of datasets.