Diagnostic Gene Biomarker Selection for Alzheimer’s Classification using Machine Learning

Abstract

Alzheimer’s disease (AD), also referred to as Alzheimer’s is a neurodegenerative disease and most common type of dementia. It starts at an older age and slowly progressive over time. It is a brain disease which causes loss of memory, reasoning and thinking capability of a person. Short-term memory loss is one of the main symptoms of the AD. Other common symptoms are said to be mood-swings, difficulty in understanding language and its interpretation etc. The major problem in the AD is, it can’t be reverted, but controllable with proper treatment. Genetic factors have a high impact on developing an AD, which can be inherited through genes. According to recent studies, gene therapy shows better results for Alzheimer’s patients than other common medications. It reduces the risk effect of the AD and has a gradual improvement on the patient’s condition. So, identification of gene biomarkers, having high involvement in developing AD could improve positive response over the treatment. In this paper, gene expressions of AD patients and normal peoples are analyzed using statistical approaches and Machine Learning (ML) algorithms. Differential Gene Expression (DEG) identification has an important part in the selection of most informative genes. Potential gene biomarkers are selected using a meta-heuristic global optimization algorithm called Rhinoceros Search Algorithm (RSA). As an outcome from RSA, 24 novel gene biomarkers are identified. Four supervised ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), Naïve Bayes (NB) and Multilayered Perceptron Neural Network (MLP-NN) are used for the classification of two different group of samples. Among them, RSA-MLP-NN model achieved 100% accuracy on identifying the distinction between AD and normal genes and proved its efficacy.